DE102021123796A1 - MAP GENERATION AND CONTROL SYSTEM - Google Patents

Abstract

One or more information cards are obtained from an agricultural work machine. The one or more information maps depict one or more agricultural characteristic values at different geographic locations of a field. An in situ sensor on the agricultural work machine senses an agricultural feature as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural trait at various locations in the field based on a relationship between the values in the one or more information maps and the agricultural trait sensed by the in situ sensor . The predictive map can be output and used in automated machine control.

Description

The present description relates to agricultural machines.

BACKGROUND

There are many different types of agricultural machinery. Some agricultural machines include harvesters such as combine harvesters, sugar cane harvesters, cotton harvesters, forage harvesters, and windrowers. Some harvesters can also be fitted with different types of headers to harvest different types of crops.

EXECUTIVE SUMMARY

One or more information cards are obtained from an agricultural work machine. The one or more information maps depict one or more agricultural characteristic values at different geographic locations of a field. An in situ sensor on the agricultural work machine senses an agricultural feature as the agricultural work machine moves through the field. A predictive map generator generates a predictive map that predicts a predictive agricultural feature at various locations in the field based on a relationship between the values in the one or more information maps and the agricultural feature sensed by the in situ sensor . The predictive map can be output and used in automated machine control.

character list

• 1 12 is a partially pictorial, partially schematic representation of an example combine harvester.
• 2 12 is a block diagram showing in more detail some portions of an agricultural harvesting machine according to some examples of the present disclosure.
• the 3a-3b (herein together as 3 ) show a flow chart illustrating an example of the operation of an agricultural harvesting machine in generating a map.
• 4A Figure 12 is a block diagram showing an example of a predictive model generator and a predictive map generator.
• 4B Figure 12 is a block diagram showing an example predictive model generator in more detail.
• 5 12 is a flowchart showing an example of an operation of a farm harvester in receiving a preliminary information map, detecting a feature with an in situ sensor, and generating a functional predictive map for display or use in controlling the farm harvester during a harvesting operation.
• 6 Figure 12 is a block diagram of an example of a control zone generator.
• 7 Figure 12 is a flow chart showing an example of the operation of the control zone generator.
• 8th Figure 12 is a flow chart showing an example of operation using control zones.
• 9 Figure 12 is a block diagram of an example operator interface control.
• 10 Fig. 12 is a flow chart showing an example of the operation of the operator interface controller.
• 11 Figure 12 is an illustration of an example user interface display.
• 12 Figure 12 is a block diagram showing an example agricultural harvester in communication with a remote server environment.
• the 13-15 show examples of mobile devices that can be used in an agricultural harvesting machine.
• 16 Figure 12 is a block diagram showing an example of a computing environment that may be used in an agricultural harvester.

DETAILED DESCRIPTION

For a better understanding of the principles of the present disclosure, reference will now be made to the examples illustrated in the drawings and specific language will be used to describe them. However, it should be understood that no limitation on the scope of the disclosure is intended. All alterations and further modifications to the described devices, systems, methods, and any further application of the principles of the present disclosure are contemplated fully as would normally occur to those skilled in the art to which the disclosure pertains. In particular, it is fully considered that the features, components Steps and/or steps described with respect to one example may be combined with the features, components, and/or steps described with respect to other examples of the present disclosure.

The present description relates to the use of in situ data taken concurrently with a farming operation in combination with previous data to generate a predictive map.

In some examples, a predictive map may be used to control an agricultural work machine, such as an agricultural harvester. As discussed above, the performance of an agricultural harvesting machine can be compromised or otherwise affected under various conditions. For example, the performance of a harvester (or other agricultural machine) may be adversely affected based on one or more of the kernel size of the harvested crop or ear, head, or husk (EHP) characteristics of the harvested crop. As used herein, core size can include various size characteristics of the core, such as diameter (such as cross-sectional width), weight, length, mass, density, volume, as well as a variety of other size characteristics or dimensions. EHP characteristics may include, but are not limited to, core, ear, piston, head, or husk deformations; Diseases in core, ear, cob, head or pod; and damage to the core, ear, cob, head, or hull, as well as dimensional characteristics of the EHP such as diameter (like section width), weight, length, mass, density, volume, and a variety of other dimensional characteristics. Sieve, chaff, cleaning fan, rotor and concave settings may vary depending on kernel size or EHP characteristics.

In some examples, a predictive biomass map may be used to control an agricultural work machine, such as an agricultural harvester. Biomass, as used herein, refers to an amount of vegetation material above the ground in a specific area or location. Often quantity is measured in terms of weight, for example weight per given area such as tons per hectare. Various traits can be indicative of biomass (referred to herein as biomass traits) and can be used to predict the biomass in a field of interest. For example, biomass properties can include various crop characteristics, such as crop height (the height of the crop above the surface of the field), crop density (the amount of crop material in a given space, which can be derived from crop mass and crop volume), crop mass (such as a weight of crop or the weight of crop components) or crop volume (how much of the given area or position is occupied by the crop, i.e. the space that the crop occupies or contains). In another example, biomass characteristics may include various machine characteristics of the agricultural harvesting machine, such as machine settings or operational characteristics. For example, a force, such as fluid pressure or torque, used to drive a threshing rotor of the agricultural harvester may be indicative of biomass.

The performance of an agricultural harvester can be affected when the agricultural harvester encroaches on areas of the field with differences in biomass. For example, when agricultural harvester machine settings are adjusted based on an expected or desired throughput, biomass variance may cause throughput to change and thus machine settings may be sub-optimal to effectively process vegetation including crop. As mentioned above, the operator can try to predict the biomass in front of the machine. Additionally, some systems, such as feedback control systems, reactively adjust the forward ground speed of the agricultural harvester to maintain a desired throughput. This can be done by attempting to identify the biomass based on sensory input, such as from sensors sensing a variable indicative of biomass. However, such arrangements can be error prone and too slow to respond to an impending biomass change to effectively alter the operation of the machine to control throughput, such as by changing the forward speed of the harvester. For example, such systems are typically reactive, making adjustments to machine settings only after vegetation has been encountered by the machine to reduce further errors, such as in a feedback control system.

A vegetative index map illustratively maps vegetative index values (which may be indicative of vegetative growth) across various geographic locations in a field of interest. An example of a vegetative index includes a normalized difference vegetative index (NDVI). There are many other vegetative indices that are within the scope of the present disclosure lying. In some examples, a vegetative index may be derived from sensor readings of one or more bands of electromagnetic radiation reflected from the plants. Without limitation, these bands can be in the microwave, infrared, visible, or ultraviolet portions of the electromagnetic spectrum.

A vegetative index map can be used to identify the presence and location of vegetation. In some examples, a vegetative index map allows crops to be identified and georeferenced in the presence of bare soil, plant debris, or other vegetation such as weeds. In other examples, a vegetative index map allows for recognizing different crop traits, such as crop growth and health or crop vigor, across different geographic locations in a field of interest.

A seed genotype map maps the genotype of seeds planted at different positions in the field. The seed genotype map can be generated by a planting machine or by a machine that performs a subsequent process, such as a spray device with an optical detector that recognizes plant genotypes.

A predictive yield map includes georeferenced predictive yield values.

A predictive weed map includes one or more georeferenced predictive weed intensity values or weed type values. The weed intensity values may include, without limitation, at least one of weed population, weed growth stage, weed size, weed biomass, weed moisture, or weed health.

A seeding map illustratively depicts seeding characteristics across various geographic locations in a field of interest. These seed charts are typically collected from previous seed planting operations in the field. In some examples, the seed map may be derived from control signals used by a seeder when the seed is planted, or from sensors on the seeder, such as sensors that confirm that a seed has been placed in a furrow made by the Seeder was created. Seeders may include geographic position sensors that geolocate the locations where the seed was planted, and topographic sensors that generate topographic information of the field. For example, the topographic sensors may include GPS, laser levels, inclinometer/odometer pairs, radio local triangulation, as well as various other systems for generating topographic information. The information generated during a previous seed planting operation can be used to determine various seed characteristics, such as location (e.g., geographic location of the planted seed in the field), spacing (e.g., spacing between each seed, distance between seed rows or both), population (which can be inferred from distance properties), seed orientation (e.g. seed orientation in a ditch or orientation of seed rows), depth (e.g .the seed depth or furrow depth), dimensions (such as seed size) or genotype (such as seed species, seed hybrids, seed varieties, etc.). Various other seed characteristics can also be determined. In some examples, seed maps may include information about the seed bed into which the seed is placed, such as soil moisture, soil temperature, soil constituents such as soil organic matter.

Alternatively or in addition to data from a previous operation, various seeding attributes may be generated on the seeding maps based on data from third parties, such as third-party seed suppliers that provide the seeds for the seed planting operation. These third parties may provide various data indicating various seed traits, for example dimensional data, such as seed size, or genotype data, such as seed types, seed hybrids, seed varieties, or seed breeding. In addition, seed providers may provide various data related to certain plant traits of the resulting plants of each different seed genotype. For example, plant growth data such as stem diameter, corn cob size, plant height, plant mass, etc., plant response to weather conditions, plant response to applied substances such as herbicides, fungicides, pesticides, insecticides, fertilizers, etc., plant response to pests, fungi, weeds, diseases, etc ., as well as any number of other plant characteristics. It should be noted that data on plant responses may include data indicative of resistance of plants to various conditions and traits, e.g. B. resistance of plants to applied substances, resistance of plants to weathering, resistance of plants to pests, fungi, weeds, diseases, etc., as well as resistance of plants to a variety of other conditions or traits.

Alternatively or in addition to the data from a pre-operation or from a third party, different seeding characteristics can be generated on the seeding maps based on different user or operator input data, for example operator or user input data specifying different seeding characteristics, such as position, depth, orientation, spacing , dimensions, genotype, as well as various other sowing characteristics.

In some examples, a seed map may be derived from sensor readings of one or more bands of electromagnetic radiation reflected from the seed or seedbed. Without limitation, these bands can be in the microwave, infrared, visible, or ultraviolet portions of the electromagnetic spectrum.

The present discussion thus continues with respect to systems that receive at least one or more of a seed map, a vegetative index map, a yield map, a biomass map, another map, and also use an in situ sensor to detect a value indicative of one or more of a kernel size or EHP trait during a harvest operation. The systems generate a model that models one or more relationships between the features derived from the advance information maps and the outputs of the in situ sensors. The one or more models are used to generate a functional predictive map that depicts a feature, such as a feature sensed by the one or more in situ sensors or associated feature, at different geographic locations in predicts the field based on the one or more advance information cards. The functional predictive map generated during the harvesting process can be used to automatically control a harvester during the harvesting process. For example, the functional predictive map can be used to control the sieve, chopper, cleaning fan, threshing rotor, and concaves. The functional predictive map can also be provided to an operator or other user.

1 12 is a partially pictorial, partially schematic representation of a self-propelled agricultural harvester 100. In the example illustrated, the agricultural harvester 100 is a combine harvester. Further, while combines are provided as examples throughout this disclosure, it should be understood that the present description is applicable to other types of harvesting machines, such as cotton harvesters, sugarcane harvesters, forage harvesters, windrowers, or other agricultural work machines. Accordingly, the present disclosure is intended to encompass the various types of harvesters described and is thus not limited to combines. Additionally, the present disclosure is directed to other types of work machines, such as agricultural seeders and sprayers, construction machines, forest machines, and lawn care machines, where predictive map generation may be applicable. Accordingly, the present disclosure is intended to encompass these various types of harvesters and other work machines and is thus not limited to combines.

As in 1 1, agricultural harvester 100 illustratively includes an operator's cab 101 that may include a variety of different operator interface mechanisms for controlling agricultural harvester 100 . The agricultural harvester 100 includes headers, such as a header 102 and a cutter, generally indicated at 104. The agricultural harvester 100 also includes a feeder housing 106, a feed accelerator 108, and a thresher, generally indicated at 110. The feeder housing 106 and the Feed accelerators 108 form part of a material handling subsystem 125 . The header 102 is pivotally coupled to a frame 103 of the agricultural header 100 along the pivot axis 105 . One or more actuators 107 power movement of header 102 about axis 105 in the direction generally indicated by arrow 109 . Thus, a vertical position of the header 102 (the header height) above the ground 111 over which the header 102 travels can be controlled by actuating the actuator 107 . Although in 1 not shown, agricultural harvesting machine 100 may also include one or more actuators operable to apply a pitch angle, a roll angle, or both to the header 102 or portions of the header 102 . Pitch refers to an angle at which the cutter assembly 104 engages the crop. The angle of inclination is increased, for example, by controlling the header 102 so that a distal edge 113 of the cutter 104 is directed more towards the ground. The angle of inclination is reduced by controlling the header 102 so that the distal edge 113 of the cutter 104 is directed further away from the ground. The roll angle refers to the orientation of the header 102 about the front-to-back longitudinal axis of the agricultural harvesting machine 100.

The thresher 110 illustratively includes a threshing rotor 112 and a set of concaves 114. Further, the agricultural harvester 100 also includes a separator 116. The agricultural harvester 100 also includes a cleaning subsystem or cleaning shoe (collectively referred to as the cleaning subsystem 118) that a cleaning fan 120, a chopper 122 and a screen 124 includes. The material handling subsystem 125 also includes a discharge drum 126, a tailings elevator 128, a clean grain elevator 130, and an unloading auger 134 and chute 136. The clean grain elevator conveys clean grain into the clean grain tank 132. The agricultural harvester 100 also includes a tailings subsystem 138 that a chopper 140 and a spreader 142. Agricultural harvester 100 also includes a propulsion subsystem that includes an engine that drives ground engaging components 144, such as wheels or tracks. In some examples, a combine may include more than one of the above subsystems within the scope of the present disclosure. In some examples, agricultural harvester 100 may include left and right hand cleaning subsystems, separators, etc., shown in 1 are not shown.

In operation and for overview, agricultural harvester 100 illustratively moves through a field in the direction indicated by arrow 147 . During movement of the agricultural harvesting machine 100, the header 102 (and the associated reel 164) engages the crop to be harvested and collects the crop towards the cutting device 104. An operator of the agricultural harvesting machine 100 can be a local human operator, a remote human operator or be an automated system. The operator of agricultural harvesting machine 100 may specify one or more height settings, pitch angle settings, or roll angle settings for header 102 . For example, the operator enters a setting or settings into a control system, described in more detail below, that controls actuator 107 . The control system may also receive a setting from the operator to set up the pitch and roll angle of the header 102 and implement the settings entered by associated actuators, not shown, that operate to change the pitch and roll angle of the header 102. The actuator 107 maintains the header 102 at a height above the ground 111 based on a height adjustment and at desired pitch and roll angles, if any. Each of the elevation, roll, and pitch settings can be implemented independently of the others. The control system responds to header errors (e.g., the difference between the elevation setting and the measured height of the header 104 above the ground 111 and, in some examples, pitch angle and roll angle errors) with a responsiveness that is determined based on a selected sensitivity level. When the sensitivity level is set to a higher sensitivity level, the control system will respond to smaller header position errors and attempt to reduce the detected errors faster than when the sensitivity is at a lower sensitivity level.

Returning to the description of the operation of agricultural harvesting machine 100, after the crop has been cut by cutter 104, the separated crop is moved by a conveyor in feed housing 106 toward feed accelerator 108, which accelerates the crop into thresher 110. The crop is threshed by the rotor 112 which rotates the crop against the concaves 114 . The threshed crop material is moved by a separator rotor in the separator 116 with a portion of the residue being moved towards the residue subsystem 138 by the discharge drum 126 . The portion of the tailings that is transferred to tailings subsystem 138 is shredded by tailings chopper 140 and spread by spreader 142 on the field. In other configurations, the residue is released from the agricultural harvester 100 in a windrow. In other examples, residue subsystem 138 may include weed control devices (not shown), such as seed baggers or other seed collectors or seed crushers or other seed destroyers.

The grain falls onto the cleaning subsystem 118. The chopper 122 separates a portion of coarser pieces of material from the grain and the screen 124 separates a portion of finer pieces of material from the clean grain. The clean grain falls onto an auger that moves the grain to an inlet end of the clean grain elevator 130 and the clean grain elevator 130 moves the clean grain up, thereby separating the clean grain in the clean grain tank 132 . Debris is removed from the cleaning subsystem 118 by the air flow of the cleaning fan 120 . The cleaning fan 120 directs air along an airflow path up through the screens and choppers. The airflow carries residue rearward in agricultural harvesting machine 100 toward residue handling subsystem 138.

The tailings elevator 128 returns the tailings to the thresher 110 where the tailings are re-threshed. Alternatively, the tailings can also be fed to a separate after-threshing mechanism by a tailings elevator or other transport device, where the tailings are also after-threshed.

1 Also shows that in one example, agricultural harvester 100 includes ground speed sensor 146, one or more separator loss sensors 148, a clean grain camera 150, a forward-looking image capture mechanism 151, which may be in the form of a stereo or mono camera, and one or more loss sensors 152 provided in the cleaning subsystem 118 is included.

The ground speed sensor 146 detects the ground speed of the agricultural harvesting machine 100 over the ground. The ground speed sensor 146 may detect the ground speed of the agricultural harvesting machine 100 by sensing the rotational speed of ground engaging components (such as wheels or tracks), a driveshaft, an axle, or other components. In some cases, vehicle speed may be detected using a positioning system, such as a global positioning system (GPS), a dead reckoning system, a remote navigation system (LORAN), a Doppler speed sensor, or a variety of other systems or sensors that provide an indication of vehicle speed. Ground speed sensors 146 may also include heading sensors such as a compass, magnetometer, gravimetric sensor, gyroscope, or GPS derivative to determine heading in two or three dimensions in combination with speed. In this way, the orientation of the agricultural harvester 100 relative to a grade is known when the agricultural harvester 100 is on a grade. For example, an orientation of the agricultural harvester 100 could include driving up, driving down, or driving across the grade. Machine or ground speed, when referred to in this disclosure, may also include two or three dimensional direction of travel.

The loss sensors 152 illustratively provide an output signal indicative of the amount of grain loss occurring on both the right and left sides of the cleaning subsystem 118 . In some examples, the sensors 152 are impact sensors that count grain impacts per unit time or per unit distance to provide an indication of grain loss occurring at the cleaning subsystem 118 . The impact sensors for the right and left sides of the shoe 118 can provide individual signals or a combined or aggregated signal. In some examples, the sensors 152 can include a single sensor, as opposed to separate sensors provided for each cleaning subsystem 118 .

The separator loss sensor 148 provides a signal indicative of grain loss in the left and right separators, in 1 not shown separately. The separator loss sensors 148 may be associated with the left and right separators and may provide separate grain loss signals or a combined or aggregated signal. In some cases, detecting grain loss in the separators can also be done using a variety of different types of sensors.

Agricultural harvester 100 may also include other sensors and measurement mechanisms. For example, the agricultural harvesting machine 100 may include one or more of the following sensors: a header height sensor that detects a height of the header 102 above the ground 111; stability sensors that detect oscillatory or bouncing motion (and amplitude) of agricultural harvesting machine 100; a residue setting sensor configured to detect whether the agricultural harvesting machine 100 is configured to shred the residue, create a windrow, etc.; a shoe box fan speed sensor to detect the speed of the fan 120; a concave distance sensor that detects the distance between the rotor 112 and the concaves 114; a threshing rotor speed sensor that detects a rotor speed of the rotor 112; a chopper gap sensor that senses the size of the openings in the chopper 122; a wire gap sensor that senses the size of the openings in the wire 124; a non-grain material (MOG) moisture sensor that detects a moisture content of the MOG passing through the agricultural harvesting machine 100; one or more machine setting sensors configured to sense various configurable settings of agricultural harvesting machine 100; a machine orientation sensor that detects the orientation of the agricultural harvesting machine 100; and crop property sensors that sense a variety of different types of crop properties, such as crop type, crop moisture, and other crop properties. The crop characteristic sensors can also be configured to detect the characteristics of the separated crop during processing by the agricultural harvesting machine 100 . For example, in some cases, the crop trait sensors may measure grain quality detect, such as broken grain, MOG values; grain components such as starches and protein; and grain feed rate as the grain moves through the feeder house 106, the clean grain elevator 130, or elsewhere in the agricultural harvester 100. The crop property sensors may also sense the rate of advance of biomass through the feeder house 106 , through the separator 116 , or elsewhere in the agricultural harvester 100 . The crop property sensors may also sense haul rate as a mass flow rate of grain through the elevator 130 or through other portions of the agricultural harvesting machine 100, or provide other output signals indicative of other sensed quantities. An internal material distribution sensor can detect the material distribution within the agricultural harvesting machine 100 .

Examples of sensors used to detect or measure the performance characteristics include a tension sensor, a current sensor, a torque sensor, a hydraulic pressure sensor, a hydraulic flow sensor, a force sensor, a bearing load sensor, and a rotation sensor. Performance characteristics can be measured at different granularities. For example, the power consumption can be recorded machine-wide, sub-system-wide or by individual components of the sub-systems.

Examples of sensors used to detect one or more of ear, head or husk (EHP) traits or kernel size include one or more cameras, capacitive sensors, piezoelectric plates, electromagnetic or ultrasonic time-of-flight Reflective sensors, signal attenuation sensors, weight or mass sensors, material flow sensors, etc. These sensors can be placed at one or more locations in the agricultural harvesting machine 100 in order to detect the distribution of material in the agricultural harvesting machine 100 during operation of the agricultural harvesting machine 100.

Examples of sensors used to detect or detect pitch or roll of the agricultural harvester 100 include accelerometers, gyroscopes, inertial measurement units, gravimetric sensors, magnetometers, etc. These sensors can also indicate the slope of the terrain on which the agricultural harvester is located 100 currently located.

A crop processing system includes a system that processes crops, and the composition of the crop processing system may vary based on whether the agricultural harvester is a combine, self-propelled field harvester, sugar cane harvester, cotton harvester, hay harvester, timber harvester, or other harvester. The functions of the crop processing system include one or more of the following functions: severing plant material from roots, severing plant material from stems, separating desirable material from undesirable material, trimming material to size, and accumulating material. In a combine, the crop processing system may include one or more of the threshing rotors 112 , concaves 114 , sieve 124 , chopper 122 , and cleaning fan 120 , in some examples without limitation.

Before describing how the agricultural harvesting machine 100 generates a functional predictive map and uses the functional predictive map for control, a brief description of some elements of the agricultural harvesting machine 100 and their operation is first provided. The description of 2 and 3 describes receiving a general type of preliminary information map and combining information from the preliminary information map with a georeferenced sensor signal generated by an in situ sensor, where the sensor signal may indicate characteristics of crop. Harvested material may include kernels, husks, heads and ears. The sensor signals may be indicative of an agricultural characteristic, such as one or more of a characteristic in the field, characteristics of crop properties, characteristics of grain, or characteristics of the agricultural harvester 100. The characteristics of the “field” include, but are not limited to, characteristics of a field such as slope , weed intensity, weed type, soil moisture, surface quality. Crop trait traits include kernel size, EHP characteristics, crop height, crop moisture, grain quality, crop density, and crop condition, among others. Grain characteristics can include grain moisture, grain size, grain test weight; and characteristics of agricultural harvester 100 may include orientation, loss levels, job quality, fuel economy, internal distribution, tailings characteristics, and power utilization, among others. A relationship between the feature values derived from in situ sensor signals and the advance information map values is identified and this relationship is used to generate a new functional predictive map. A functional predictive map predicts values at various geographic locations in a field, and one or more of those values can be used to control a machine. In some cases, a functional predictive map may be presented to a user, such as an operator of an agricultural work machine, which may be an agricultural harvester. A functional predictive map can be presented to a user visually, such as via a display, haptic, or audible. The user can interact with the functional predictive map to perform editing operations and other user interface operations. In some cases, a functional predictive map can be used both to control an agricultural work machine, such as an agricultural harvester, presentation to an operator or other user, and presentation to an operator or user for interaction by the operator or user.

After the general approach regarding the 2 and 3 , a more specific approach to generating a functional predictive map that can be presented to an operator or user or used to control agricultural harvesting machine 100 or both is described with reference to FIG 4 and 5 described. Although the present discussion continues with respect to the agricultural harvester, and more particularly a combine harvester, other types of agricultural harvesters or other agricultural work machines are within the scope of the present disclosure.

2 FIG. 12 is a block diagram showing some portions of an exemplary agricultural harvesting machine 100. FIG. 2 shows that agricultural harvesting machine 100 illustratively includes one or more processors or servers 201, data storage 202, geographic position sensor 204, communication system 206, and one or more in situ sensors 208 that detect one or more agricultural characteristics simultaneously with a harvesting operation to capture. An agricultural trait can include any trait that can affect the harvesting process. Some examples of agricultural characteristics include characteristics of the agricultural harvester, the field, the crops in the field, and the weather. Other types of agricultural characteristics are also considered. The in situ sensors 208 generate values corresponding to the sensed features. Agricultural harvester 100 also includes a predictive model or relationship generator (hereinafter collectively referred to as "predictive model generator 210"), a predictive map generator 212, a control zone generator 213, a control system 214, one or more controllable subsystems 216, and an operator interface mechanism 218. The Agricultural harvester 100 may also include a variety of other agricultural harvester functions 220 . The in situ sensors 208 include, for example, onboard sensors 222, remote sensors 224, and other sensors 226 that sense characteristics throughout a farming operation. The predictive model generator 210 illustratively includes a prior information variable-to-in situ variable model generator 228 , and the predictive model generator 210 may include other elements 230 . The control system 214 includes the communication system control 229, the operator interface control 231, an adjustment control 232, the path planning control 234, the feed rate control 236, the header and reel control 238, the draper belt control 240, the top plate position control 242, the residue system control 244, the machine cleaning control 245, the zone control 247 , and system 214 may include other elements 246 . The controllable subsystems 216 include machine and header actuators 248, the propulsion subsystem 250, the steering subsystem 252, the residue subsystem 138, the machine cleaning subsystem 254, and the subsystems 216 may include a variety of other subsystems 256.

2 Also shows that the agricultural harvester 100 can receive a pre-information card 258 . As described below, the advance information map 258 includes, for example, a map of a previous operation in the field, such as an unmanned or manned aerial vehicle or other ground vehicle. The preliminary information map 258 may include one or more of a seed map, a vegetative index (VI) map, a yield map, a biomass map, a weed map, or another map. However, the pre-map information may also include other types of data obtained prior to a harvest operation or a pre-operation map. 2 also shows that an operator 260 can operate the agricultural harvesting machine 100 . The operator 260 interacts with the operator interface mechanisms 218. In some examples, the operator interface mechanisms 218 may include joysticks, levers, a steering wheel, linkages, pedals, buttons, knobs, keypads, user actuable elements (such as icons, buttons, etc.) on a user interface display device, a microphone and speaker (if speech recognition and speech synthesis are provided), as well as a variety of other types of control devices. If a touch-sensitive display system is provided, operator 260 may interact with operator interface mechanisms 218 interact using touch gestures. These examples described above are provided as illustrative examples and are not intended to limit the scope of the present disclosure. Accordingly, other types of operator interface mechanisms 218 may be used and are within the scope of the present disclosure. However, the pre-map information may also include other types of data obtained prior to a harvest operation or a pre-operation map. 2 also shows that an operator 260 can operate the agricultural harvesting machine 100 . The operator 260 interacts with the operator interface mechanisms 218. In some examples, the operator interface mechanisms 218 may include joysticks, levers, a steering wheel, linkages, pedals, buttons, knobs, keypads, user actuable elements (such as icons, buttons, etc.) on a user interface display device, a microphone and speaker (if speech recognition and speech synthesis are provided), as well as a variety of other types of control devices. When a touch-sensitive display system is provided, operator 260 may interact with operator interface mechanisms 218 using touch gestures. These examples described above are provided as illustrative examples and are not intended to limit the scope of the present disclosure. Accordingly, other types of operator interface mechanisms 218 may be used and are within the scope of the present disclosure.

The preliminary information map 258 may be downloaded to the agricultural harvester 100 and stored in the data storage 202 using the communication system 206 or otherwise. In some examples, communication system 206 may be a cellular communication system, a system for communicating over a wide area network or a local area network, a system for communicating over a near field communication network, or a communication system configured to communicate over any of a variety of other networks or combinations to communicate from networks. The communication system 206 may also include a system that facilitates downloading or transferring information to and from a Secure Digital (SD) card or a universal serial bus (USB) card, or both.

The geographic position sensor 204 illustratively senses or detects the geographic position or location of the agricultural harvesting machine 100. The geographic position sensor 204 may include, but is not limited to, a global navigation satellite system (GNSS) receiver that receives signals from a GNSS satellite transmitter. The geographic position sensor 204 may also include a real-time kinematics (RTK) component configured to improve the accuracy of the position data derived from the GNSS signal. The geographic position sensor 204 may include a dead reckoning system, a cellular triangulation system, or a variety of other geographic position sensors.

The in situ sensors 208 can be any of the sensors described above with respect to FIG 1 act. The in situ sensors 208 include onboard sensors 222 mounted onboard the agricultural harvester 100 . Such sensors may include, for example, a speed sensor (e.g., a GPS, tachometer, or compass), image sensors located within agricultural harvesting machine 100 (such as the clean grain camera, or cameras mounted to monitor one or more of material distribution , kernel size, or EHP traits in the agricultural harvesting machine 100, e.g., in the residue subsystem or the cleaning system), or other kernel size sensors or EHP trait sensors. The in situ sensors 208 also include remote in situ sensors 224 that collect in situ information. In situ data includes data collected by a sensor onboard the harvester or any sensor that detects the data during the harvesting process.

The predictive model generator 210 generates a model that indicates a relationship between the values sensed by the in situ sensor 208 and a feature mapped by the prior information map 258 onto the field. For example, if prior information map 258 maps a yield trait to various locations in the field and in situ sensor 208 detects a value indicative of kernel size, then prior information variable-to-in situ variable model generator 228 generates a predictive model , which models the relationship between yield characteristics and kernel size. The predictive machine model may also be generated based on features from one or more of the advance information maps 258 and one or more in situ data values generated by in situ sensors 208 . Then, the predictive map generator 212 uses the predictive model generated by the predictive model generator 210 to generate a functional predictive map 263 that represents the value of a feature, such as a kernel size or EHP feature, measured by the in situ sensors 208 different positions in the field based on the preliminary information map 258 predicted.

In some examples, the type of values in functional predictive map 263 may be the same as the type of in situ data sensed by in situ sensors 208 . In some cases, the type of values in the functional predictive map 263 may have different units than the data collected from the in situ sensors 208 . In some examples, the type of values in functional predictive map 263 may be the same as the type of in situ data sensed by in situ sensors 208 but related to the type of data sensed by in situ sensors 208 have data type. For example, the type of data captured by the in situ sensors 208 may indicate the type of values in the functional predictive map 263 in some examples. In some examples, the data type in functional predictive map 263 may differ from the data type in advance information map 258 . In some cases, the type of data in functional predictive map 263 may have different units than the data in advance information map 258 . In some examples, the type of data in the functional predictive map 263 may be different than the type of data in the prior information map 258 but related to the type of data in the prior information map 258 . For example, the data type in advance information map 258 in functional predictive map 263 in some examples may be indicative of the data type in functional predictive map 263 in some examples. In some examples, the data type in the functional predictive map 263 differs from one or both of the in situ data type sensed by the in situ sensors 208 and the data type in the advance information map 258. In some examples, the data type is in the functional predictive map 263 is the same as one or both of the in situ data type sensed by the in situ sensors 208 and the data type in the advance information map 258. In some examples, the data type in the functional predictive map 263 is the same as that of the In situ sensors 208 detected in situ data type or the data type in the advance information card 258 and is different from the other.

The predictive map generator 212 may use the features in the prior information map 258 and the model generated by the predictive model generator 210 to generate a functional predictive map 263 that predicts the features at various locations in the field. The predictive map generator 212 thus outputs the predictive map 264 .

As in 2 As shown, predictive map 264 predicts the value of a detected feature (detected by in situ sensors 208) or a feature related to the detected feature at various locations across the field based on a prior information value in prior information card 258 ahead of these positions and the predictive model. For example, if the predictive model generator 210 has generated a predictive model indicating a relationship between a yield trait and kernel size, then given the yield traits at various locations across the field, the predictive map generator 212 generates a predictive map 264 indicating the value of the kernel size different positions across the field. The yield trait obtained from the yield map at those locations and the relationship between the yield trait and the kernel size obtained from the predictive model are used to generate predictive map 264 . The predicted kernel size can be used by a control system to adjust, for example, one or more sieve and chopper openings, rotor operation, concave gap, or cleaning fan speed.

Some variations in the types of data depicted in the preliminary information map 258, the types of data collected by in situ sensors 208, and the types of data predicted on the predictive map 264 will now be described. These are just examples to illustrate that the data types can be the same or different.

In some examples, the type of data in advance information map 258 differs from the type of data captured by in situ sensors 208, yet the type of data in predictive map 264 is the same as the type of data captured by in situ sensors 208 will. For example, the preliminary information map 258 may be a topographical map and the variable sensed by the in situ sensors 208 may be a grain quality attribute. The predictive map 264 may then be a predictive machine map that maps predicted machine feature values to various geographic locations in the field.

Also, in some examples, the type of data in the advance information map 258 differs from the type of data sensed by in situ sensors 208, and the type of data in the predictive map 264 differs from both the type of data in the advance information map 258 and the type of data , which is detected by the in situ sensors 208 . For example, the preliminary information map 258 may be a topographical map and the variable sensed by the in situ sensors 208 may be a pitch/roll of the machine. The predictive map 264 can then be a predictive internal distribution map mapping predicted internal distribution values to various geographic locations in the field.

In some examples, the pre-information map 258 is from a pre-operation through the field and the type of data is different than the type of data captured by in situ sensors 208, however, the type of data in the predictive map 264 is the same as the type of data captured by the in situ sensors 208 is detected. For example, the preliminary information map 258 may be a seed genotype map generated during planting, and the variable sensed by the in situ sensors 208 may be loss. The predictive map 264 may then be a predictive loss map that maps predicted grain loss values to various geographic locations in the field. In another example, the preliminary information map 258 may be a seed genotype map, and the variable sensed by the in situ sensors 208 may be crop condition, such as stalled crop or crop kinked. The predictive map 264 may then be a predictive crop condition map that maps predicted crop condition values to various geographic locations in the field.

In some examples, the pre-information map 258 is from a pre-operation through the field and the data type is the same as the data type sensed by in situ sensors 208 and the data type in the predictive map 264 is also the same as the data type, sensed by the in situ sensors 208 . For example, advance information map 258 may be a yield map generated during a previous year, and the variable sensed by in situ sensors 208 may be yield. The predictive map 264 may then be a predictive yield map that maps predicted yield values to various geographic locations in the field. In such an example, the relative yield differences in the georeferenced pre-information map 258 from the previous year may be used by the predictive model generator 210 to generate a predictive model that models a relationship between the relative yield differences on the pre-information map 258 and the yield values determined by In -situ sensors 208 are detected during the current harvesting process. The predictive model is then used by the predictive map generator 210 to generate a predictive yield map.

In some examples, a predictive map 264 may be provided to the control zone generator 213 . Control zone generator 213 groups contiguous discrete point data values on predictive map 264 into control zones. A control zone may include two or more contiguous sections of an area, such as a field, for which a control parameter corresponding to the control zone for controlling a controllable subsystem is constant. For example, a response time for changing a setting of controllable subsystems 216 may be insufficient to respond satisfactorily to changes in values contained in a map, such as predictive map 264. In this case, control zone generator 213 analyzes the map and identifies control zones, which have a defined size in order to take the response time of the controllable subsystems 216 into account. In another example, the control zones may be sized to reduce wear from excessive actuator movement resulting from continuous adjustment. In some examples, there may be a different set of control zones for each controllable subsystem 216 or for groups of controllable subsystems 216. The control zones can be added to the predictive map 264 to obtain a predictive control zone map 265 . Thus, the predictive control zone map 265 may be similar to the predictive map 264, except that the predictive control zone map 265 includes control zone information defining the control zones. Thus, a functional predictive map 263 as described herein may include control zones. Both predictive map 264 and predictive control zone map 265 are functional predictive maps 263. In one example, functional predictive map 263 does not include control zones, such as predictive map 264.

In another example, a functional predictive map 263 includes control zones, such as predictive control zone map 265. In some examples, multiple crops may be present in a field at the same time when a mixed crop production system is implemented. In this case, the predictive map generator 212 and the control zone generator 213 can identify the position and characteristic of the two or more crop types and then generate the predictive map 264 and the predictive control zone map 265 accordingly.

It should also be noted that the control zone generator 213 can group values to generate control zones and the control zones can be added to a predictive tax zone map 265 or a separate map showing only the generated control zones. In some examples, the control zones may be used only to control or calibrate agricultural harvester 100, or both. In other examples, the control zones may be presented and used by the operator 260 to control or calibrate agricultural harvester 100, and in other examples, the control zones may simply be presented to operator 260 or another user, or stored for later use.

The predictive map 264 or the predictive control zone map 265 or both are provided to the control system 214, which generates control signals based on the predictive map 264 or the predictive control zone map 265 or both. In some examples, the communication system controller 229 controls the communication system 206 to communicate the predictive map 264 or the predictive control zone map 265 or control signals based on the predictive map 264 or the predictive control zone map 265 to other agricultural harvesting machines harvesting in the same field. In some examples, communication system controller 229 controls communication system 206 to send predictive map 264, predictive control zone map 265, or both to other remote systems.

In some examples, a predictive map 264 may be provided to the route/order generator 267 . The route/job generator 267 records a route for the agricultural harvester 100 to drive based on the predictive map 264 during harvesting operations. The route may also contain machine control settings that also correspond to locations along the route. For example, if a roadway leads up a hill, at some point prior to the hill climb, the roadway may include a controller that indicates power is directed to drive systems to maintain a speed or haul rate of the agricultural harvesting machine 100 . In some examples, the route/job generator 267 analyzes the various orientations of the agricultural harvesting machine 100 and the predicted machine characteristics that are intended to produce the orientations according to the predictive map 264 for a variety of different travel routes and selects a route that exhibits desirable results ( such as a fast harvest time or desired power utilization or material distribution uniformity).

Operator interface controller 231 is operable to generate control signals to control operator interface mechanisms 218 . Operator interface control 231 is also operable to present operator 260 with predictive map 264 or predictive control zone map 265 or other information derived from or based on predictive map 264, predictive control zone map 265, or both. The operator 260 can be a local operator or a remote operator. As an example, controller 231 generates control signals to control a display mechanism to display one or both of predictive map 264 and predictive control zone map 265 to operator 260 . Controller 231 may create operator actuable mechanisms that are displayed and operable by the operator to interact with the displayed map. The operator can edit the map by, for example, correcting a power utilization displayed on the map based on the operator's observation. The adjustment controller 232 may generate control signals to control various adjustments on the agricultural harvesting machine 100 based on the predictive map 264, the predictive control zone map 265, or both. For example, the adjustment controller 232 may generate control signals to control the machine and header actuators 248 . In response to the generated control signals, the machine and header actuators 248 operate to adjust, for example, one or more of the sieve and chopper settings, the concave gap, the threshing rotor settings, the cleaning fan speed settings, the header height, the header functionality, the reel speed, the reel position, the draper functionality (wherein the agricultural harvester 100 is coupled to a draper header), corn header functionality, internal distribution control, and other actuators 248 that affect the other functions of the agricultural harvester 100 to control. Path planning controller 234 illustratively generates control signals to control steering subsystem 252 to steer agricultural harvester 100 according to a desired path. Path planning controller 234 may control a path planning system to generate a route for agricultural harvester 100 and may control propulsion subsystem 250 and steering subsystem 252 to steer agricultural harvester 100 along that route. Feedrate controller 236 may control various subsystems, such as propulsion subsystem 250 and machine actuators 248, to control feedrate based on predictive map 264 or predictive control zone map 265, or both. For example, when agricultural harvester 100 approaches sloping terrain that has an estimated speed value above a selected threshold, feed rate controller 236 may reduce the speed of machine 100 to maintain a constant feed rate of biomass through agricultural harvester 100 . The header and reel control 238 may generate control signals to control a header or reel or other header functionality. The draper band controller 240 may generate control signals to control a draper band or other draper functionality based on the predictive map 264, the predictive control zone map 265, or both. For example, when agricultural harvester 100 approaches sloping terrain that has an estimated speed value above a selected threshold, draper belt controller 240 may increase the speed of the draper belts to prevent material from backing up on the belts. Deck position controller 242 may generate control signals to control a position of a deck included in a header based on predictive map 264 or predictive control zone map 265, or both, and tailing system controller 244 may generate control signals to position a tailing subsystem 138 based on a predictive map 264 or a predictive control zone map 265 or both. The machine cleaning controller 245 may generate control signals to control the machine cleaning subsystem 254 . For example, if agricultural harvesting machine 100 is about to move across a slope where it is estimated that internal material distribution will be disproportionately to one side of cleaning subsystem 254, machine cleaning controller 245 may adjust cleaning subsystem 254 to to account for or correct the disproportionate material. Other controls included in agricultural harvesting machine 100 may also control other subsystems based on predictive map 264 or predictive control zone map 265 or both.

the 3A and 3B (herein together as 3 ) show a flow chart illustrating an example of the operation of the agricultural harvesting machine 100 in generating a predictive map 264 and a predictive control zone map 265 based on the preliminary information map 258 .

At 280, agricultural harvester 100 receives the advance information card 258. Examples of the advance information card 258 or receiving the advance information card 258 are discussed with respect to blocks 281, 282, 284, and 286. As discussed above, the preliminary information map 258 maps values of a variable corresponding to a first feature to various positions in the field, as indicated at block 282 . As indicated at block 281, receiving the advance information card 258 may include selecting one or more of a plurality of possible advance information cards that are available. For example, a preliminary information map may be a terrain profile map generated from air phase profilometry images. Another preliminary information map may be a map generated during a previous pass through the field, which may have been performed by another machine that performed a previous operation in the field, such as a sprayer or other machine. The process by which one or more pre-information cards are selected can be manual, semi-automated, or automatic. The preliminary information map 258 is based on data collected prior to a current harvesting operation. Block 284 indicates this. For example, the data may have been collected by a GPS receiver mounted on a piece of equipment during a pre-field operation. For example, the data in a lidar area scan may be collected during a previous year or earlier in the current growing season or at other times. The data may be based on data detected or received in ways other than using lidar range scanning. For example, a drone equipped with a fringe projection profilometry system can capture the profile or elevation of the terrain. Or, for example, some topographical features can be estimated based on weather patterns, such as the formation of furrows due to erosion or the breaking up of clumps over freeze-thaw cycles. In some examples, the preliminary information map 258 may be created by combining data from a variety of sources, such as those listed above. For example, the data for the preliminary information map 258 , such as a topographical map, may be transmitted to the agricultural harvester 100 using the communication system 206 and stored in the data store 202 . The data for the preliminary information card 258 can also be provided in other ways using the communication system 206 of the agricultural harvesting machine 100, as indicated by block 286 in the flow chart of FIG 3 is shown. In some examples, advance information card 258 may be received by communication system 206 .

At the beginning of a harvesting operation, the in situ sensors 208 generate sensor signals indicative of one or more in situ data values indicative of a machine characteristic, such as kernel size and EHP characteristics. Examples of in situ sensors 208 are discussed with respect to blocks 222, 290 and 226. As discussed above, in situ sensors 208 include onboard sensors 222; Remote in situ sensors 224, such as UAV-based sensors, flown at a time used to collect in situ data, as shown in block 290; or other types of in situ sensors, denoted by in situ sensors 226 . In some examples, data from onboard sensors is georeferenced using position, heading, or speed data from geographic location sensor 204 .

The predictive model generator 210 controls the prior information variable-to-in situ variable model generator 228 to generate a model representing a relationship between the mapped values contained in the prior information map 258 and the in situ values that by the in situ sensors 208 are modeled as indicated by block 292 . The features or data types represented by the mapped values in preliminary information map 258 and the in situ values sensed by in situ sensors 208 may be the same features or data types or different features or data types.

The relationship or model generated by the predictive model generator 210 is provided to the predictive map generator 212 . The predictive map generator 212 generates a predictive map 264 that represents a value of the feature sensed by the in situ sensors 208 at various geographic locations in a field to be harvested or another feature that is consistent with the sensed by the in situ sensors 208 feature is predicted using the predictive model and the advance information card 258 as indicated by block 294 .

Note that in some examples, the preliminary information card 258 may include two or more different cards or two or more different card layers of a single card. Each map in the two or more different maps, or each layer in the two or more different map layers of a single map, maps a different type of variable to the geographic locations in the field. In such an example, the predictive model generator 210 generates a predictive model that models the relationship between the in situ data and each of the various variables depicted by the two or more different maps or the two or more different map layers. Likewise, the in situ sensors 208 may include two or more sensors, each sensing a different type of variable. Thus, the predictive model generator 210 generates a predictive model that models the relationships between each variable type represented by the prior information map 258 and each variable type sensed by the in situ sensors 208 . The predictive map generator 212 may generate a functional predictive map 263 that includes a value for each sensed feature sensed by the in situ sensors 208 (or a feature related to the sensed feature) at different locations in the Field that will be harvested using the predictive model and each of the maps or map layers in the advance information map 258 predicts.

The predictive map generator 212 configures the predictive map 264 such that the predictive map 264 is actionable (or consumable) by the control system 214 . Predictive map generator 212 may provide predictive map 264 to control system 214 or control zone generator 213, or both. Some examples of different ways in which predictive map 264 may be configured or issued are described with respect to blocks 296, 293, 295, 299, and 297. For example, the predictive map generator 212 configures the predictive map 264 so that the predictive map 264 includes values that can be read by the control system 214 and used as a basis for generating control signals for one or more of the various controllable subsystems of the agricultural harvesting machine 100, such as indicated by block 296.

The route/job generator 267 records a route for the agricultural harvester 100 to drive on during harvesting operations based on the predictive map 204 as indicated at block 293 . The control zone generator 213 may divide the predictive map 264 into control zones based on the values on the predictive map 264 . Continuously geolocated values that are within a threshold of each other can be grouped into a tax zone. The threshold may be a default threshold, or the threshold may be set based on operator input, based on input from an automated system, or based on other criteria. A size of the zones may be based on a responsiveness of the control system 214, the controllable subsystems 216, or wear and tear considerations, or other criteria, as indicated by block 295. The predictive map generator 212 configures the predictive map 264 for presentation to an operator or other user. The control zone generator 213 can configure a predictive control zone map 265 for presentation to an operator or other user. Block 299 indicates this. When presented to an operator or other user, the presentation of predictive map 264 or predictive control zone map 265, or both, may include one or more of the predictive values on predictive map 264 that are correlated to the geographic location, the control zones on the predictive control zone map 265 that are correlated to the geographic position and contain adjustment values or control parameters that are used based on the predicted values on the map 264 or the zones on the predictive control zone map 265. In another example, the presentation may include more abstracted information or more detailed information. The representation may also include a confidence level that indicates an accuracy with which the predicted values on the predictive map 264 or the zones on the predictive control zone map 265 match measured values that can be measured by sensors on the agricultural harvesting machine 100 when moving the agricultural harvesting machine 100 through the field. Furthermore, an authentication or authorization system can be provided that implements authentication and authorization processes when information is presented at multiple locations. For example, there may be a hierarchy of people who are authorized to view and change maps and other presented information. For example, an onboard display device may only display the maps locally on the machine in near real time, or the maps may also be generated at one or more remote locations. In some examples, each physical display device at each location may be associated with a person or user permission level. The user permission level can be used to determine what indicator marks are visible on the physical display device and what values that person can change. For example, a local operator of machine 100 may not be able to view the information corresponding to predictive map 264 or make changes to machine operation. However, a supervisor at a remote location can see the predictive map 264 on the display but not make any changes. A manager, who may be at a separate remote location, may be able to see all of the items on the predictive map 264 and also be able to change the predictive map 264 used in machine control. This is an example of an authorization hierarchy that can be implemented. The predictive map 264 or the predictive control zone map 265 or both can also be configured in other ways, as indicated by block 297 .

At block 298, inputs from the geographic position sensor 204 and other in situ sensors 208 are received by the control system. Block 300 represents the control system 214 receiving an input from the geographic position sensor 204 identifying a geographic position of the agricultural harvesting machine 100 . Block 302 represents the receipt by the control system 214 of sensor inputs indicative of the trajectory or heading of the agricultural harvester 100, and Block 304 represents the receipt by the control system 214 of a speed of the agricultural harvester 100. Block 306 represents the receipt of other information from various in situ sensors 208 by the control system 214.

At block 308 the control system 214 generates control signals to control the controllable subsystems 216 based on the predictive map 264 or the predictive control zone map 265 or both and the input from the geographic position sensor 204 and any other in situ sensors 208 . At block 310, the control system 214 applies the control signals to the controllable subsystems. It is understood that the particular control signals that are generated and the particular controllable subsystems 216 that are controlled may vary based on one or more different things. For example, the control signals generated and the controllable subsystems 216 that are controlled may be based on the type of predictive map 264 or predictive control zone map 265 or both used. Likewise, the control signals generated and the controllable subsystems 216 that are controlled and the timing of the control signals may be based on various latencies of crop flow through the agricultural harvesting machine 100 and the responsiveness of the controllable subsystems 216.

For example, a generated predictive map 264 in the form of a functional predictive core size map may be used to control one or more subsystems 216 . For example, the functional predictive kernel size map may include kernel size values georeferenced to positions within the field to be harvested. The kernel size values from the functional predictive kernel size map can be extracted and used to control the fan speed to ensure that the cleaning fan 120 minimizes crop losses through the cleaning subsystem 118 as the agricultural harvester 100 moves through the field. For example, the cleaning fan 120 can be controlled by reducing the fan speed to avoid blowing smaller grain out of the agricultural harvesting machine 100 . In another example, the predictive map 264 is an EHP trait map that includes EHP trait values indicative of diseased crop georeferenced to various locations in the field to be harvested. The EHP feature values can be extracted and used to control the speed of the cleaning fan 120 by increasing the fan speed to blow the diseased crop out of the agricultural harvester 100. The foregoing examples relating to use of a predictive core size map and the predictive EHP feature map are provided by way of example only. Accordingly, a variety of other control signals may be generated using values obtained from a predictive engine map or other type of predictive map to control one or more of the controllable subsystems 216 .

At block 312, it is determined whether the harvesting operation has been completed. If harvesting is not complete, processing continues at block 314 where in situ sensor data from geographic location sensor 204 and in situ sensors 208 (and possibly other sensors) continue to be read.

In some examples, at block 316, agricultural harvester 100 may also recognize learning trigger criteria to perform machine learning on one or more of predictive map 264, predictive control zone map 265, the model generated by predictive model generator 210, the zones generated by generated by control zone generator 213, one or more control algorithms implemented by controllers in control system 214, and other triggered learning.

The learning trigger criteria can contain a large number of different criteria. Some examples of detecting trigger criteria are discussed with respect to blocks 318, 320, 321, 322, and 324. For example, in some examples, the triggered learning may include restoring a relationship used to generate a predictive model when a threshold amount of in situ sensor data is obtained from the in situ sensors 208 . In such examples, receipt of an amount of in situ sensor data from in situ sensors 208 that exceeds a threshold triggers or causes predictive model generator 210 to generate a new predictive model used by predictive map generator 212 will. Thus, as agricultural harvester 100 continues a harvesting operation, receipt of the threshold amount of in situ sensor data from in situ sensors 208 triggers the generation of a new relationship represented by a predictive model generated by predictive model generator 210 is produced. Furthermore, the new predictive map 264, the predictive control zone map 265, or both can be regenerated using the new predictive model. Block 318 represents detecting a threshold set of in situ sensor data used to trigger creation of a new predictive model.

In other examples, the learning trigger criteria may be based on how much the in situ sensor data from the in situ sensors 208 changes from previous values or from a threshold. For example, if deviations within the in situ sensor data (or the relationship between the in situ sensor data and the information in the preliminary information map 258) are within a range that is less than a defined amount or below a threshold, then no new predictive model is generated by the predictive model generator 210. As a result, the predictive map generator 212 does not generate a new predictive map 264, predictive control zone map 265, or both. However, if deviations within the in situ sensor data exceed the range or, for example, exceed the predefined amount or threshold, or if a relationship between the in situ sensor data and the information in the advance information map 258 varies, for example, by a defined amount, the predictive Model generator 210 creates a new predictive model using all or part of the newly received in situ sensor data, which predictive map generator 212 uses to generate a new predictive map 264 . At block 320, variations in the in situ sensor data, such as a magnitude of an amount by which the data exceeds the selected range or a magnitude of variation in the relationship between the in situ sensor data and the information in the preliminary information map 258, used as a trigger to cause the generation of a predictive model and map. The threshold, range, and defined amount may be set to default values, or set by operator or user interaction via a user interface, or set by an automated system, or otherwise.

Other learning trigger criteria can also be used. For example, if the predictive model generator 210 switches to a different advance information map (different from the originally selected advance information map 258), switching to the other advance information map may require relearning by the predictive model generator 210, the predictive map generator 212, the control zone generator 213, the Trigger control system 214 or other elements. In another example, the transition of the agricultural harvesting machine 100 to a different topography or to another control zone can be used as learning trigger criteria.

In some cases, operator 260 may also edit predictive map 264 or predictive control zone map 265, or both. The edits may change a value on predictive map 264, or the size, shape, location, or presence of a control zone, or a value on predictive control zone map 265, or both. Block 321 shows that processed information can be used as learning trigger criteria.

Also, in some cases, the operator 260 may observe that automated control of a controllable subsystem is not what the operator desires. In such cases, the operator 260 may provide the controllable sub-system with an operator-initiated adjustment that reflects the operator 260 wanting the controllable sub-system to operate differently than is commanded by the control system 214 . Thus, an operator-initiated change to a setting by operator 260 may cause predictive model generator 210 to relearn a model, predictive map generator 212 to regenerate map 264, control zone generator 213 to regenerate the control zones on predictive control zone map 265, and that Control system 214 relearns its control algorithm or performs machine learning on one of the control components 232-246 in control system 214 based on adjustment by operator 260, as shown in block 322. Block 324 represents the use of other triggered learning criteria.

In other examples, the relearning may be performed periodically or intermittently, for example based on a selected time interval, such as a discrete time interval or a variable time interval. Block 326 indicates this.

When relearning is triggered, whether based on learning trigger criteria or based on the passage of a time interval, as indicated by block 326, one or more of the predictive model generator 210, the predictive map generator 212, the control zone generator 213 and the control system 214 performs machine learning to generate a new predictive model, map, control zones, or control algorithm based on the learning trigger criteria. The new predictive model, map and control algorithm are generated using additional data collected since the last learning. Performing the relearn is indicated by block 328 .

When the harvesting process has been completed, the process proceeds from block 312 to block 330 where one or more of the predictive map 264, the predictive control zone map 265, and the predictive model generated by the predictive model generator 210 are stored. The predictive map 264, predictive control zone map 265, and predictive model may be stored locally on data storage 202 or sent to a remote system using communication system 206 for later use.

It should be noted that while some examples herein describe the predictive model generator 210 and the predictive map generator 212 using a prior information map when generating a predictive model or receiving a functional predictive map, the predictive model generator 210 and the predictive map generator 212 in other examples in generating a predictive model and a functional predictive map, other types of maps including predictive maps such as a functional predictive map generated during the harvesting process may receive.

4A is a block diagram of a portion of the in 1 shown agricultural harvesting machine 100. In particular 4A among other things, an example of the predictive map generator 212 in more detail. 4A also illustrates the flow of information between the various components shown. The predictive model generator 210 receives a prior information map 258. The prior information map 258 includes values of an agricultural characteristic corresponding to various geographic locations in the field. The advance information map 258 may include one or more of a seed map 335, a VI map 336, a yield map 338, a biomass map 340, or another map 342, in some examples. The predictive model generator 210 also receives a geographic position 334 or an indication of a geographic position from the geographic position sensor 204. The in situ sensors 208 detect a value of an agricultural feature that is indicative of one or more of the core quantity or an EHP feature. The in situ sensors 208 may thus include one or more of the kernel size sensors 344 that sense a size of crop kernels, or the EHP trait sensor 346 that senses an EHP trait, and a processing system 352 . In some cases, the one or more sensors 344 and 346 may be onboard the agricultural harvester 100 . The processing system 352 processes sensor data generated by the one or more sensors 344 and 346 to form processed data 354, some examples of which are described below.

In some examples, one or more sensors 344 and 346 can generate electronic signals indicative of the feature the sensor is sensing. The processing system 352 processes one or more of the sensor signals received via the sensors to generate processed data identifying one or more features. The features identified by processing system 352 may include kernel size or an EHP feature.

Sensors 344 and 346 may be or include optical sensors, such as a camera located within agricultural harvester 100 (hereinafter “process camera”) that displays internal portions of agricultural harvester 100 that are processing the agricultural material for grain . Thus, in some examples, processing system 352 is operable to determine one or more of the core size and one or more EHP characteristics of material passing through agricultural harvester 100 based on an image captured by sensors 344 and 346. to recognize. In other examples, the process camera may be a clean grain camera 150 and the processing system 352 is operable to detect kernel size and EHP features.

Other sensors can also be used. In some examples, raw or processed data from sensors 344 and 346 may be presented to operator 260 via operator interface mechanism 218 . The operator 260 may be onboard the agricultural harvester 100 or at a remote location.

4B 12 is a block diagram showing an example of a model generator 210 in more detail. in the in 4B For example, as illustrated, the predictive model generator 210 may include one or more of the vegetative index-to-seed-size model generator 366, the vegetative-index-to-EHP trait model generator 368, the seeded-trait-to-seed-size model generator 372, the seeded-trait-to- EHP trait model generator 374, the yield-to-kernel-sizes model generator 380, the yield-to-EHP trait model generator 382, the biomass-to-kernel-sizes model generator 388, the biomass-to-EHP trait model generator 390 , the combination model generator 404 and other elements 406 . Each of the model generators included in 4B 1, creates a model that models a relationship between values on an information card 259, which may be a pre-information card or a predictive card or other type of card, and values sensed by an in situ sensor 208. The combination model generator 404 can generate one or more models based on various combinations of one or more information cards 259 and inputs from one or more in situ sensors 208 .

Kernel size can be affected by things like plant health, which can be indicated by the vegetative index. The vegetative index to kernel size model generator 366 may generate a model that models a relationship between the VI features on the VI map 336 and the kernel size sensor 344 output. Also, when harvesting rapeseed, for example, it can be difficult to generate a fan speed that will hold all of the grain but blow out all of the husks, stalks, pith, etc. The effectiveness of this blower in doing so may depend on the size and filling of the kernels, for example in areas of poor vegetative health, as indicated by vegetative index features on VI map 336, the kernels may be smaller and more at risk of being blown out. Thus, the relationship between features on VI chart 336, such as vegetative health, and the kernel size sensed by sensor 344 can be used by model generator 366 to create a model that models that relationship.

In addition, VI values can be related to EHP characteristics. Thus, the vegetative index-to-EHP feature model generator 368 can generate a model that models the relationship between the values on the VI map 336 and the EHP feature sensor 346 output.

Different sowing characteristics, such as e.g. B. seeding population traits or seed genotype, may affect or otherwise share a relationship with kernel size. For example, different plant genotypes may have different kernel size traits. Population characteristics can also be inversely related to the resulting core size. Smaller cores can be more prone to losses. Therefore, the seeding trait-to-kernel size model generator 372 can generate a relationship between seeding trait values, such as population or genotype, on the seeding map 335 and kernel size sensor values generated by the kernel size sensor 344 .

Likewise, seeding traits may have an effect on, or otherwise share a relationship with, various EHP traits. For example, different genotypes can differ in terms of deformities, disease resistance and damage resistance behave differently. The seed feature-to-EHP feature model generator 374 thus creates a model that models a relationship between the seed features on the seed map 335 and the EHP features sensed by the EHP feature sensor 346 .

Yield may also be related to kernel size. For example, higher yielding areas in a field may show larger cores than lower yielding areas. Thus, the yield-to-kernel size model generator 380 can generate a model that models a relationship between the predicted yield values on the yield map 338 and the outputs from the kernel size sensor 344 .

Yield may also be related to EHP traits. For example, increased yields may be related to healthier crops. Therefore, the yield-to-EHP trait model generator 382 may generate a model that models a relationship between the predicted yield values on the yield map 338 and the EHP traits sensed by the EHP trait sensor 346 .

The amount of biomass processed by agricultural harvester 100 may also be related to kernel size. For example, an increase in biomass being processed by agricultural harvester 100 at any given time may indicate more robust crops with larger kernels. Thus, in some examples, various machine settings, such as screen, chopper, and cleaning fan settings, may be controlled based on kernel size to reduce potential grain loss. Therefore, the biomass-to-nucleus size model generator 388 may generate a model that models a relationship between biomass feature values on the biomass map 340 and the kernel size sensed by the kernel size sensor 344 .

Biomass may also be related to EHP traits. Diseased, damaged, or deformed plants can exhibit different biomass characteristics. Thus, the biomass-to-EHP feature model generator 390 may generate a model that models a relationship between biomass features on the biomass map 340 and EHP features sensed by the EHP feature sensor 346 .

There can also be other relationships. Therefore, the combination model generator 404 can generate a model that models a relationship between the feature on one or more of the information cards 259 and one or more of the sensors 344 and 346 .

Referring again to 4A For example, the predictive map generator 212 may include one or more of the kernel size map generator 410, the EHP feature map generator 412, the combination map generator 414, and other elements 417. A number of examples of different combinations of in situ sensors 208 and information cards 259 will now be described.

The predictive model generator 210 is operable to generate a predictive model 408 or to generate a plurality of predictive models 408, such as one or more of the predictive models described in FIG 4B shown model generators are generated. In another example, two or more of the predictive models described above can be combined into a single predictive model that includes two or more features, for example, core size or EHP features, based on the features of one or more of the information cards 259 at different locations in the field predicted. Each of these machine models or combinations thereof are collectively represented by the machine model 408 in 4A shown.

The predictive model 408 is provided to the predictive map generator 212 . In the example of 4A predictive map generator 212 includes a kernel size map generator 410, an EHP feature map generator 412, and a combination map generator 414. In other examples, the predictive map generator 212 may include additional, fewer, or different map generators. Thus, in some examples, the predictive map generator 212 may include other elements 417, which may include other types of map generators to generate maps for other types of features.

Kernel size map generator 410 illustratively generates a predictive kernel size map 418 that predicts the kernel size at various locations in the field based on the information values at those locations in the field indicated by information map 259 and predictive model 408 .

The EHP feature map generator 412 illustratively generates a predictive EHP map 420 that predicts EHP features at various locations in the field based on the features in the information map 259 at those locations in the field and the predictive model 408 .

The combination map generator 414 illustratively generates a predictive combination map 422 that predicts combinations of features at different locations based on the features in combinations of the information maps 259 at those locations on the field and the predictive model 408 .

The predictive map generator 212 outputs as the predictive map 264 one or more predictive maps 418, 420, 422 that predict a feature. Each of the predictive maps 418, 420, 422 predicts the respective feature at different positions in a field. Predictive map 264 (which may be one or more of maps 418, 420, 422) may be provided to control zone generator 213, control system 214, or both. The control zone generator 213 generates control zones and integrates these control zones into the functional predictive map, i. H. the predictive maps 418, 420, 422 to provide a predictive map 418 with tax zones, a predictive map 420 with tax zones and a predictive map 422 with tax zones. One or more of the predictive maps 418, 420 and 422 with or without control zones can be provided to the control system 214, which generates control signals to control one or more of the controllable subsystems 216 based on the one or more predictive maps 418, 420 and 422 with or without control zones.

5 Figure 4 is a flowchart of an example of the operation of predictive model generator 210 and predictive map generator 212 in generating predictive model 408 and predictive feature maps 418, 420, and 422. At block 430, predictive model generator 210 and predictive map generator 212 receive an information map 259, the one or more of the in 4A information cards shown may be. At block 432, processing system 352 receives one or more sensor signals from in situ sensors 208. As discussed above, the in situ sensor may be a core size sensor 344 or an EHP feature sensor 346.

At block 434, the processing system 352 processes the one or more received sensor signals to generate data indicative of a feature. In some cases, as indicated at block 436, the sensor data may be indicative of core size. In some cases, as indicated at block 438, the sensor data may be indicative of one or more EHP characteristics.

At block 444, the predictive model generator 210 also obtains the geographic location 334 that corresponds to the sensor data. For example, the predictive model generator 210 may obtain the geographic position from the geographic position sensor 204 and determine an accurate geographic position based on machine delays, machine speed, etc. where the sensor data was collected or derived. Additionally, at block 444, the orientation of the agricultural harvester 100 in the field may be determined. For example, the orientation of the agricultural harvester 100 may be obtained to identify its orientation relative to the slope in the field.

At block 446, the predictive model generator 210 generates one or more predictive models, such as the machine model 408, that establish a relationship between one or more features on an information map 259 and a feature detected by the in situ sensor 208 or a related feature will, model.

At block 448, the predictive model, such as predictive model 408, is provided to predictive map generator 212, which generates a functional predictive map that maps a predictive feature based on information map 259 and predictive model 408. In some examples, the functional predictive map is a kernel size predictive map 418. In some examples, the functional predictive map is an EHP predictive map 420. In some examples, the functional predictive map is a combination predictive map 422.

The functional predictive map can be generated during the course of a farming operation. Thus, when an agricultural harvester moves through a field in which a farming operation is being performed, the functional predictive map is generated while the farming operation is being performed.

At block 450, the predictive map generator 212 outputs the functional predictive map. At block 452, the predictive map generator 212 outputs the functional predictive map for presentation and possible interaction by the operator 260. At block 454, the predictive map generator 212 may configure the functional predictive map for the control system 214 to consume. At block 456, the predictive map generator 212 may also provide the control zone generator 213 with the functional predictive map for generating control zones. At block 458, the predictive map generator 212 configures the functional predictive map in other ways as well. The functional predictive map (with or without the control zones) is provided to the control system 214 . At block 460, the control system 214 generates control signals to the controllable subsystems 216 on to control the basis of the functional predictive map.

The control system 124 can generate control signals to control actuators which themselves control one or more of the speed at which the screen 124 and chopper 122 oscillate, the size of the openings in the screen 124 and chopper 122, the speed of the cleaning fan 120 and the rotor 112 which drives the rotor pressure and the gap between the rotor 112 and the concaves 114 or other things.

Thus, it can be seen that the present system creates one or more information maps that map features to various locations in a field. The present system also uses one or more in situ sensors that collect in situ sensor data indicative of a feature and creates a model that represents a relationship between the feature detected using the in situ sensor or a related feature and the feature depicted in the information map. Thus, the present system generates a functional predictive map using a model, in situ data and an information map, and can configure the generated functional predictive map for consumption by a control system or for presentation to a local or remote operator or other user. For example, the control system can use the map to control one or more systems of an agricultural harvester.

6 FIG. 12 is a block diagram illustrating an example control zone generator 213. FIG. The control zone generator 213 includes a work machine actuator (WMA) selector 486, a control zone generation system 488, and a regime zone generation system 490. The control zone generator 213 may include other elements 492 as well. The control zone generation system 488 includes the control zone criteria identification component 494, the control zone boundary definition component 496, the target setting identification component 498 and other elements 520. The regime zone generation system 490 includes the regime zone criteria identification component 522, the regime zone boundary definition component 524, the setting resolver identification component 526 and other elements 528. Before describing the overall operation of the control zone generator 213 in more detail , a brief description of some of the elements of the control zone generator 213 and their respective operation is first provided.

The agricultural harvesting machine 100 or other work machine may include a variety of different types of controllable actuators that perform different functions. The controllable actuators on agricultural harvesting machine 100 or other work machines are collectively referred to as work machine actuators (WMAs). Each WMA can be independently controllable based on values on a functional predictive map, or the WMAs can be controlled as sets based on one or more values on a functional predictive map. Therefore, the control zone generator 213 can generate control zones corresponding to each individually controllable WMA or to the sets of WMAs that are controlled in concert with one another.

The WMA selector 486 selects a WMA or set of WMAs for which to create corresponding control zones. The control zone creation system 488 then creates the control zones for the selected WMA or set of WMAs. Different criteria may be used in identifying control zones for each WMA or group of WMAs. For example, for a WMA, the WMA response time can be used as a criterion for defining the boundaries of the control zones. In another example, wear characteristics (e.g., how badly a particular actuator or mechanism wears as a result of its movement) may be used as a criterion for identifying control zone boundaries. The control zone criteria identification component 494 identifies certain criteria to be used in defining control zones for the selected WMA or set of WMAs. The control zone boundary definition component 496 processes the values on a functional predictive map being analyzed to define the boundaries of the control zones on that functional predictive map based on the values in the functional predictive map being analyzed and based on the control zone criteria for the selected WMA or define a set of WMAs.

The target setting identification component 498 sets a value of the target setting used to control the WMA or set of WMAs in different control zones. For example, if the selected WMA is the cleaning fan 120 and the analyzed functional predictive map is a functional predictive kernel size map 418, the target setting in each control zone may be a target fan speed setting based on kernel size values contained in the functional predictive kernel size map 418 within the identified control zone .

In some examples where the agricultural harvester 100 is to be controlled based on a current or future position of the agricultural harvester 100, multiple target settings for a WMA at a particular position may be possible. In this case, the target settings can have different values and compete. Therefore, the target settings must be resolved so that only a single target setting can be used to control the WMA. For example, if the WMA is the cleaning fan 120 being controlled to control the speed of the cleaning fan 120, there may be several different competing sets of criteria used by the control zone generation system 488 in identifying the control zones and target settings for the selected WMA in the Tax zones are taken into account. For example, different target settings for controlling fan speed may be based on, for example, a recognized or predicted feed rate value, a recognized or predicted core attribute value, such as a recognized or predicted kernel size attribute value, a recognized or predicted EHP attribute value, a recognized or predicted fuel efficiency value, a recognized or predicted grain loss value or a combination thereof. However, at any given time, the agricultural harvester 100 cannot control the cleaning fan 120 to operate at multiple speeds simultaneously. Rather, the agricultural harvesting machine 100 controls the cleaning fan 120 to operate at a single speed at all times. Thus, one of the competing target settings is selected to control the speed of the cleaning fan 120 in the agricultural harvesting machine 100 .

In some examples, regime zone generation system 490 generates regime zones to resolve multiple different competing target settings. The regimen zone criteria identification component 522 identifies the criteria used to identify regimen zones for the selected WMA or set of WMAs on the functional predictive map to be analyzed. Some criteria that can be used to identify or define regime zones include, for example, crop type or crop variety based on a stand map or other source of crop type or crop variety, weed type, weed intensity, crop condition, such as whether the crop is overturned, in part kinked or upright, yield, biomass, vegetative index, kernel size, EHP traits or topography. Just as each WMA or group of WMAs may have a corresponding control zone, different WMAs or groups of WMAs may have a corresponding regime zone. The regime zone boundary definition component 524 identifies the boundaries of regime zones on the functional predictive map being analyzed based on the regime zone criteria identified by the regime zone criteria identification component 522 .

In some examples, regime zones may overlap. For example, an EHP feature regime zone may overlap with part or all of a core size regime zone. In such an example, the various regime zones may be assigned a precedent hierarchy such that when two or more regime zones overlap, the regime zone that has been assigned a greater hierarchical position or importance in the precedent hierarchy takes precedence over the regime zones that have a lower hierarchical position position or importance in the precedent hierarchy. The regime zone priority hierarchy may be set manually or automatically using a rule-based system, a model-based system, or some other system. As an example, when a core size regimen zone overlaps with an EHP feature regimen area, the core size regimen area may be assigned greater importance in the hierarchy of precedence than the EHP feature regimen area, such that the core size regimen area takes precedence.

In addition, each regime zone may have a unique setting resolver for a specific WMA or set of WMAs. The adjustment resolver identification component 526 identifies a particular adjustment resolver for each regimen zone identified on the functional predictive map to be analyzed and a particular adjustment resolver for the selected WMA or set of WMAs.

• Wenn vorhergesagte Erntegutfeuchtigkeitswerte innerhalb von 6 Metern (200 Fuß) vom Erntevorsatz der landwirtschaftlichen Erntemaschine 100 größer als x sind (wobei x ein ausgewählter oder vorbestimmter Wert ist), dann ist der Zieleinstellwert zu verwenden, der auf Grundlage der Vorschubgeschwindigkeit gegenüber anderen konkurrierenden Zieleinstellungen ausgewählt wird, andernfalls ist der Zieleinstellwert auf Grundlage des Kornverlusts gegenüber anderen konkurrierenden Zieleinstellwerten zu verwenden.

Once the attitude resolver is identified for a particular regime zone, that attitude resolver can be used to resolve competing target attitudes, identifying more than one target attitude based on the control zones. The different types of setting resolvers can have different shapes. For example, the setting resolvers identified for each regime zone may include a human choice resolver in which the competing target settings are presented to an operator or other user for resolution. In another example, the attitude resolver may be a neural network or other artificial intelligence or include a machine learning system. In such cases, the setting resolvers may resolve the competing target settings based on a predicted or historical quality metric corresponding to each of the different target settings. For example, an increased fan speed setting can improve grain purity and increase grain loss. A reduced cleaning fan speed setting can reduce grain purity and reduce grain loss. If grain loss or grain quality is selected as the quality metric, given the two competing fan speed setting values, the predicted or historical value for the selected quality metric can be used to resolve the speed setting. In some cases, the adjustment resolvers can be a set of threshold rules that can be used instead of or in addition to the regime zones. An example of a threshold rule can be expressed as follows:

• If predicted crop moisture values within 6 meters (200 feet) of the header of agricultural harvesting machine 100 are greater than x (where x is a selected or predetermined value), then use the target setting selected based on feed rate versus other competing target settings , otherwise use target setting based on grain loss versus other competing target setting.

The attitude resolvers can be logical components that execute logical rules in identifying a target attitude. For example, the attitude resolver may resolve target settings while attempting to minimize harvest time, or minimize total harvest cost, or maximize grain harvested, or based on other variables calculated depending on the various target settings in question. Harvest time can be minimized when an amount to complete a harvest is reduced to a selected threshold or below. Total harvesting costs can be minimized when the total harvesting costs are reduced to or below a selected threshold. Crop can be maximized when the amount of crop is increased to or above a selected threshold.

7 12 is a flowchart illustrating an example of the operation of control zone generator 213 in generating control zones and regime zones for a map that control zone generator 213 receives for zone processing (e.g., for a map that is being analyzed).

At block 530, the control zone generator 213 receives a map that is analyzed for processing. In one example, as shown at block 532, the map analyzed is a functional predictive map. For example, the map analyzed may be one of functional predictive maps 418, 420, or 422. Block 534 indicates that the card to be analyzed can be other cards.

At block 536, the WMA selector 486 selects a WMA or set of WMAs for which to create control zones on the analyzed map. At block 538, the control zone criteria identification component 494 obtains control zone definition criteria for the selected WMAs or set of WMAs. Block 540 provides an example where the control zone criteria is or includes wear characteristics of the selected WMA or set of WMAs. Block 542 provides an example where the control zone definition criteria is or includes magnitude and variation of input source data, such as magnitude and variation of values on the analyzed map or magnitude and variation of inputs from various in situ sensors 208. Block 544 gives an example where the control zone definition criteria are or include physical machine characteristics, such as the physical dimensions of the machine, a speed at which various subsystems operate, or other physical machine characteristics. Block 546 provides an example where the control zone definition criteria is or includes a responsiveness of the selected WMA or set of WMAs in meeting newly commanded adjustment values. Block 548 provides an example where the control zone definition criteria are or include machine performance metrics. Block 550 provides an example where the control zone definition criteria is or includes operator preferences. Block 552 provides an example where the tax zone definition criteria is or includes other elements. Block 549 provides an example where the tax zone definition criteria are time-based, meaning that agricultural harvester 100 does not cross the boundary of a tax zone until a selected period of time has elapsed since agricultural harvester 100 entered a particular tax zone. In some cases, the selected length of time may be a minimum length of time. Thus, in some cases, the control zone definition criteria may prevent agricultural harvester 100 from crossing a boundary of a control zone until at least the selected period of time has elapsed. Block 551 provides an example where the tax zone definition criteria is based on a selected size value. For example, a tax zone definition criteria based on a selected size value may include the definition of a Exclude tax zone smaller than selected size. In some cases, the size selected may be a minimum size.

At block 554, regimen zone criteria identification component 522 obtains regimen zone definition criteria for the selected WMA or set of WMAs. Block 556 provides an example where the regime zone definition criteria are based on manual input from operator 260 or another user. Block 558 illustrates an example where the regime zone definition criteria are based on crop type or crop variety. Block 560 illustrates an example where the regimen zone definition criteria are based on core size. Block 562 illustrates an example where the regime zone definition criteria are based on or include crop status. Block 564 provides an example where the regime zone definition criteria is or includes other criteria.

At block 566, the tax zone boundary definition component 496 creates the boundaries of tax zones on the map to be analyzed based on the tax zone criteria. The regime zone boundary definition component 524 creates the boundaries of regime zones on the map to be analyzed based on the regime zone criteria. Block 568 provides an example in which the zone boundaries for the control zones and regime zones are identified. Block 570 shows that the target setting identification component 498 identifies the target settings for each of the zones of control. The control zones and regime zones may be created in other ways, as indicated by block 572.

At block 574 , the adjustment resolver identification component 526 identifies the adjustment resolver for the selected WMAs in each regime zone defined by the regime zone boundary definition component 524 . As discussed above, the regime zone resolver may be a human resolver 576, an artificial intelligence or machine learning system resolver 578, a predicted or historical quality-based resolver 580 for each competing target setting, a rule-based resolver 582, a performance criteria-based resolver 584, or other resolver 586 be.

At block 588, the WMA selector 486 determines whether there are more WMAs or sets of WMAs to process. If additional WMAs or sets of WMAs remain to be processed, processing returns to block 436 where the next WMA or set of WMAs for which control zones and regime zones are to be defined is selected. If no additional WMAs or sets of WMAs remain for which to generate control zones or regime zones, processing moves to block 590 where the control zone generator 213 outputs a map of control zones, target settings, regime zones, and setting resolvers for each of the WMAs or sets of WMAs . As discussed above, the issued card may be presented to operator 260 or another user; the card issued may be provided to the control system 214; or the issued card may be otherwise issued.

8th 12 illustrates an example of the operation of the control system 214 in controlling the agricultural harvesting machine 100 based on a map output from the control zone generator 213 . Thus, at block 592, the control system 214 receives a map of the work site. In some cases, as represented by block 594, the map may be a functional predictive map that may include control zones and regime zones. In some cases, the received map may be a functional predictive map excluding tax zones and regime zones. Block 596 provides an example where the received worksite map may be an information map with tax zones and regime zones identified thereon. The information map may be a preliminary information map or a predictive map. Block 598 provides an example where the received map may include multiple different maps or multiple different map layers. Block 610 provides an example where the received map may take other forms as well.

At block 612, the control system 214 receives a sensor signal from the geographic position sensor 204. The sensor signal from the geographic position sensor 204 may include data indicating the geographical position 614 of the agricultural harvester 100, the speed 616 of the agricultural harvester 100, the heading 618 of the agricultural harvester 100 or provide other information 620 . At block 622, the zone controller 247 selects a regime zone, and at block 624, the zone controller 247 selects a control zone on the map based on the geographic position sensor signal. At block 626, the zone controller 247 selects a WMA or set of WMAs to control. At block 628, the zone controller 247 obtains one or more target settings for the selected WMA or set of WMAs. The target settings obtained for the selected WMA or group of WMAs can come from different sources. For example, block 630 depicts an example where one or more of the target settings for the selected WMA or set of WMAs are identified input from the tax zones on the job site map. Block 632 depicts an example where one or more of the target settings are obtained from human input from the operator 260 or another user. Block 634 shows an example where the target settings are obtained from an in situ sensor 208 . Block 636 depicts an example where the one or more target settings are obtained from one or more sensors on other machines operating in the same field, either concurrently with agricultural harvesting machine 100 or from one or more sensors on machines operating in the past worked in the same field. Block 638 shows an example where the target settings are also obtained from other sources.

At block 640, the zone controller 247 accesses the adjustment resolver for the selected regimen zone and controls the adjustment resolver to resolve competing target adjustments into a resolved target adjustment. As discussed above, in some cases, the setting resolver may be a human resolver, with zone controller 247 controlling operator interface mechanisms 218 to present the competing target settings to operator 260 or another user for resolution. In some cases, the attitude resolver may be a neural network or other artificial intelligence or machine learning system, and the zone controller 247 communicates the competing target attitudes to the neural network, artificial intelligence or machine learning system for selection. In some cases, the attitude resolver may be based on a predicted or historical quality metric, threshold rules, or logical components. In one of these latter examples, the zone controller 247 executes the adjustment resolver to obtain a resolved target adjustment based on the predicted or historical quality metric, based on the threshold rules, or using the logical components.

At block 642, if the zone controller 247 has identified the resolved target setting, the zone controller 247 provides the resolved target setting to other controllers in the control system 214, which generate and apply control signals based on the resolved target setting to the selected WMA or set of WMAs. For example, if the selected WMA is a machine or header actuator 248, the zone controller 247 provides the resolved target setting to the adjustment controller 232 or the header/reel controller 238 or both to generate control signals based on the resolved target setting, and these generate control signals are applied to the machine or header actuators 248. If at block 644 additional WMAs or additional sets of WMAs are to be controlled at the current geographic position of agricultural harvester 100 (as detected at block 612), processing returns to block 626 where the next WMA or set of WMAs is selected. The processes represented by blocks 626 through 644 continue until all WMAs or sets of WMAs that are to be controlled at the current geographic position of agricultural harvesting machine 100 have been addressed. If there are no additional WMAs or sets of WMAs to control at the current geographic location of agricultural harvester 100, processing proceeds to block 646 where zone controller 247 determines whether there are additional control zones to consider in the selected regime zone. If there are additional tax zones to consider, processing returns to block 624 where a next tax zone is selected. If there are no more additional control zones to consider, processing transfers to block 648 which determines whether there are any additional regime zones to consider. The zone controller 247 determines whether additional regimen zones are to be considered. If there are more regimen zones to consider, processing returns to block 622 where a next regimen zone is selected.

At block 650, zone controller 247 determines whether the operation that agricultural harvester 100 is performing is complete. If not, as indicated by block 652, the zone controller 247 determines whether a control zone criteria has been met to continue processing. For example, as noted above, the control zone definition criteria may include criteria that define when a control zone boundary may be crossed by agricultural harvesting machine 100 . For example, a selected period of time may define whether a control zone boundary may be crossed by agricultural harvester 100, meaning that agricultural harvester 100 is prevented from crossing a zone boundary until a selected period of time has elapsed. In this case, at block 652, the zone controller 247 determines whether the selected time period has expired. In addition, the zone controller 247 can perform the processing continuously. Thus, the zone controller 247 does not wait a certain period of time before proceeding to determine whether operation of the agricultural harvesting machine 100 is complete. At block 652, the zone controller 247 determines that it is time to continue processing, and then processing continues at block 612 where the zone controller 247 again receives input from the geographic position sensor 204 . It should also be noted that the zone controller 247 may control the WMAs and sets of WMAs simultaneously using a multiple-input, multiple-output controller, rather than sequentially controlling the WMAs and sets of WMAs.

9 FIG. 14 is a block diagram showing an example of an operator interface controller 231. FIG. In an illustrated example, the operator interface control 231 includes an operator input command processing system 654, another control interaction system 656, a voice processing system 658, and an action signal generator 660. The operator input command processing system 654 includes the voice processing system 662, the touch gesture manipulation system 664, and other elements 666. The other control interaction system 656 includes the control input processing system 668 and the control output generator 670. The speech processing system 658 includes the trigger detector 672, the recognition component 674, the synthesis component 676, the natural language understanding system 678, the dialogue management system 680 and other elements 682. The action signal generator 660 includes the visual control signal generator 684, the audio control signal generator 686, the haptic control signal generator 688 and other elements 690. Before operation de r in 9 While the operator interface control 231 shown is described in handling various operator interface actions, a brief description of some of the elements of the operator interface control 231 and the operation associated therewith is first provided.

Operator input command processing system 654 recognizes operator inputs at operator interface mechanisms 218 and processes those inputs for commands. Speech processing system 662 recognizes speech input and processes the interactions with speech processing system 658 to process the speech input for commands. Touch gesture handler system 664 recognizes touch gestures on touch-sensitive elements in operator interface mechanisms 218 and processes those inputs for commands.

The other controller interaction system 656 handles interactions with other controllers of the control system 214. The controller input processing system 668 recognizes and processes inputs from other controllers in the control system 214, and the controller output generator 670 generates outputs and provides those outputs to other controllers in the control system 214. Speech processing system 658 recognizes speech inputs, determines the meaning of those inputs, and provides an output that indicates the meaning of the spoken inputs. For example, voice processing system 658 may recognize voice input from operator 260 as a setting change command, in which operator 260 commands control system 214 to change a setting for controllable subsystem 216 . In such an example, voice processing system 658 recognizes the content of the spoken command, identifies the meaning of that command as a setting change command, and provides the meaning of that input back to voice processing system 662. Voice processing system 662, in turn, interacts with control output generator 670 to provide the commanded output to the provide appropriate control in control system 214 to comply with the spoken setting change command.

The language processing system 658 can be invoked in a number of ways. For example, voice processing system 662 continuously provides input from a microphone (which is one of operator interface mechanisms 218) to voice processing system 658 in one example. The microphone detects speech from the operator 260 and the speech processing system 662 provides the speech processing system 658 with the recognized speech. Trigger detector 672 detects a trigger indicating that language processing system 658 is invoked. In some cases, when speech processing system 658 receives continuous speech input from speech processing system 662, speech recognition component 674 performs continuous speech recognition on all speech spoken by operator 260. In some cases, the language processing system 658 is configured to be invoked using a wake-up word. That is, in some cases, operation of the speech processing system 658 may be initiated based on recognition of a selected spoken word, referred to as a wake-up word. In such an example, where the detection component 674 detects the wakeup word, the detection component 674 provides an indication that the wakeup word was detected to trigger the detector 672 . The trigger detector 672 detects that the voice processing system 658 has been invoked or triggered by the wake-up word. In another example, the language processing system 658 may be invoked by an operator 260 operating an actuator on a user interface mechanism, such as by touching an actuator on a touch-sensitive display screen, by pressing a button, or by providing another trigger input. In such an example, trigger detector 672 may detect that language processing system 658 has been invoked when a trigger input is detected via a user interface mechanism. Trigger detector 672 may also detect that speech processing system 658 has been invoked in other ways.

Once the speech processing system 658 is invoked, the speech input from the operator 260 is provided to the speech recognition component 674 . Speech recognition component 674 recognizes linguistic elements in speech input, such as words, phrases, or other linguistic units. The natural language understanding system 678 identifies a meaning of the recognized language. The meaning can be a natural language output, a command output identifying a command reflected in the recognized language, a value output identifying a value in the recognized language, or a variety of other outputs reflecting understanding of the recognized language. For example, the natural language understanding system 678 and the language processing system 568 can more generally understand the meaning of the recognized language in the context of the agricultural harvesting machine 100 .

In some examples, voice processing system 658 may also generate outputs that navigate operator 260 through a user experience based on voice input. For example, the dialog management system 680 can create and manage a dialog with the user to identify what the user wants to do. Dialogue can make a user's command unmistakable; identify one or more specific values required to execute the user's command; or receive other information from the user or provide other information to the user or both. Synthesis component 676 may generate speech synthesis that may be presented to the user through an audio operator interface mechanism, such as a speaker. Thus, the dialogue managed by dialogue management system 680 may be spoken dialogue only or a combination of both visual dialogue and spoken dialogue.

Action signal generator 660 generates action signals to control operator interface mechanisms 218 based on outputs from one or more of operator input command processing system 654, other control interaction system 656, and language processing system 658. Visual control signal generator 684 generates control signals to control visual elements in operator interface mechanisms 218 . The visual elements can be lights, a display screen, warning indicators, or other visual elements. Audio control signal generator 686 generates outputs that control audio elements of operator interface mechanisms 218 . The audio elements include a speaker, audible alarm mechanisms, horns, or other audible elements. The haptic control signal generator 688 generates control signals that are output to control haptic elements of the operator interface mechanisms 218 . The haptic elements include vibration elements that can be used to vibrate, for example, the operator's seat, steering wheel, pedals or joysticks used by the operator. The haptics may include tactile feedback or force feedback elements that provide a tactile feedback or force feedback signal to the operator via operator interface mechanisms. The haptics may also include a variety of other haptics.

10 12 is a flow chart illustrating an example of the operation of operator interface controller 231 in generating an operator interface display on operator interface mechanism 218, which may include a touch-sensitive display screen. 10 also illustrates an example of how operator interface controller 231 may recognize and process operator interactions with the touch-sensitive display screen.

At block 692, operator interface controller 231 receives a card. Block 694 gives an example where the map is a functional predictive map and block 696 gives an example where the map is another map type. At block 698 , operator interface controller 231 receives input from geographic position sensor 204 identifying the geographic position of agricultural harvester 100 . As indicated in block 700 , the input from geographic position sensor 204 may include heading along with the position of agricultural harvester 100 . Block 702 provides an example where the input from geographic location sensor 204 includes the speed of agricultural harvester 100, and block 704 provides an example where the input from geographic location sensor 204 includes other items.

At block 706, visual control signal generator 684 in operator interface controller 231 controls the touch-sensitive display screen in operator interface mechanisms 218 to generate a display showing all or part of a field represented by the received card. Block 708 indicates that the displayed field may include a current position marker showing a current position of agricultural harvesting machine 100 relative to the field. Block 710 provides an example where the displayed field includes a next unit of work marker that identifies a next unit of work (or area in the field) in which agricultural harvesting machine 100 is operating. Block 712 provides an example in which the displayed field includes an upcoming area display section that indicates areas that are yet to be processed by the agricultural harvesting machine 100, and Block 714 provides an example in which the displayed field includes previously visited display sections, represent the areas of the field that the agricultural harvesting machine 100 has already worked. Block 716 provides an example in which the displayed panel displays various features of the georeferenced location panel on the map. For example, if the received map is a kernel size map, the displayed field may display the different categories of the size of kernels that exist in the field that have been georeferenced within the displayed field. The mapped features may be shown in the previously visited areas (as shown in block 714), in the upcoming areas (as shown in block 712), and in the next work unit (as shown in block 710). Block 718 provides an example where the displayed field also includes other items.

11 FIG. 7 is a pictorial representation showing an example of a user interface display 720 that may be generated on a touch-sensitive display screen. In other implementations, user interface display 720 may be generated on other types of displays. The touch-sensitive screen can be mounted in the cab of the agricultural harvesting machine 100 or on the mobile device or elsewhere. The user interface display 720 will be described prior to the description of the in 10 shown flowchart is continued.

in the in 11 In the example shown, the user interface display 720 illustrates that the touch-sensitive display screen includes a display feature for operating a microphone 722 and a speaker 724 . Thus, the touch-sensitive display may be communicably coupled to microphone 722 and speaker 724 . Block 726 indicates that the touch-sensitive display screen may include a variety of operator interface control actuators, such as buttons, keyboards, soft keyboards, links, icons, switches, etc. The operator 260 may manipulate the operator interface control actuators to perform various functions.

in the in 11 In the example shown, user interface display 720 includes a field display portion 728 that displays at least a portion of the field in which agricultural harvesting machine 100 is operating. Field display portion 728 is shown with a current position marker 708 corresponding to a current position of agricultural harvesting machine 100 in the portion of the field shown in field display portion 728 . In one example, the operator can control the touch-sensitive display to zoom into portions of panel display portion 728 or to pan or scroll panel display portion 728 to view different portions of the panel. A next unit of work 730 is shown as the area of the field just ahead of the current position marker 708 of the agricultural harvester 100 . The current position marker 708 may also be configured to identify the direction of travel of the agricultural harvesting machine 100, a travel speed of the agricultural harvesting machine 100, or both. In 11 the shape of the current position marker 708 provides an indication of the orientation of the agricultural harvester 100 within the field, which can be used as an indication of a direction of travel of the agricultural harvester 100 .

The size of the next work unit 730 highlighted on the panel display portion 728 may vary based on a variety of different criteria. For example, the size of the next work unit 730 may vary depending on the ground speed of the agricultural harvester 100 . Thus, if the agricultural harvester 100 is moving faster, the range of the next working unit 730 may be larger than the range of the next working unit 730 if the agricultural harvester 100 is moving slower. Panel display portion 728 is also shown displaying previously visited area 714 and upcoming areas 712 . Previously visited areas 714 represent areas that have already been harvested, while upcoming areas 712 represent areas that have yet to be harvested. The panel display portion 728 is also shown, displaying various features of the panel. in the in 11 illustrated example the displayed map is a kernel size map. Therefore, a variety of different core size marks are displayed on the panel display section 728 . There is a set of kernel size indicator markers 732 that are displayed in the areas 714 already visited. There is also a set of core size indicator marks 732 shown in upcoming areas 712 and there is a set of core size indicator marks 732 shown in next work unit 730 . 11 Figure 12 shows that the kernel size indicator marks 732 consist of various symbols indicating an area of similar kernel size. in the in 3 In the example shown, the ! symbol represents areas of large kernel size; the * symbol represents areas of medium core size; and the # symbol represents an area of small kernel size. Thus, the panel display section 728 displays various measured or predicted kernel sizes located at various regions within the panel. As previously described, the indicator markings 732 may consist of various symbols, and as described below, the symbols may be any display feature, such as different colors, shapes, patterns, intensities, text, symbols, or other display features. In some cases, each position of the field may have an indicator mark associated with it. Thus, in some cases, an indicator mark may be provided at each location of the tile display portion 728 to identify the type of feature being imaged for any particular location of the tile. Accordingly, the present disclosure contemplates providing an indication marker, such as loss level indicator marker 732 (as described in the context of the present example in FIG 11 ) at one or more positions on the field display portion 728 to identify the type, degree, etc. of the displayed feature, thereby identifying the feature at the corresponding position in the displayed field.

In the example of 11 the user interface display 720 also has a control display section 738 . Control display portion 738 allows the operator to display information and interact with the display of user interface 720 in various ways.

For example, the actuators and indicator markers in section 738 may be displayed as individual items, fixed lists, scrollable lists, drop-down menus, or drop-down lists. in the in 11 In the example shown, the display section 738 displays information for the three different core sizes corresponding to the three symbols mentioned above. The display portion 738 also includes a set of touch-sensitive actuators that the operator 260 can interact with by touch. For example, the operator 260 may touch the touch-sensitive actuators with a finger to activate the respective touch-sensitive actuator.

A flags column 739 shows flags that have been set automatically or manually. Flag actuator 740 allows operator 260 to mark a current location and then add information indicating the core size found at the current location. For example, when the operator 260 operates the flag actuator 740 by touching the flag actuator 740, the touch gesture handler system 664 in the operator interface controller 231 identifies the current position as a position where the agricultural harvester 100 has encountered a large kernel size. When the operator 260 touches the button 742, the touch gesture handler system 664 identifies the current location as a location where the agricultural harvesting machine 100 encountered an intermediate kernel size. When the operator 260 touches the button 744, the touch gesture handler system 664 identifies the current location as a location where the agricultural harvesting machine 100 encountered a small kernel size. The touch gesture handler system 664 also controls the visual control signal generator 684 to mark the point, beginning, end or continuation of a position corresponding to the identified core size on the field display 728 section 728 when the keys 740, 742 or 744 are actuated. For example, upon actuation of button 740, 742, or 744, the visual control signal generator may add an icon corresponding to the identified kernel size on panel display portion 728.

Column 746 displays the symbols corresponding to each core size category tracked on panel display portion 728 . The identifier column 748 shows the identifier (which may be a textual identifier or other identifier) that identifies the core size category. Without limitation, the kernel size symbols in column 746 and the identifiers in column 748 can include any indicia characteristics, such as different colors, shapes, patterns, intensities, text, symbols, or other indicia characteristics. The values displayed in column 750 may be a predicted core size or a core size measured by in situ sensors 208 . In one example, operator 260 can select the particular portion of field display portion 728 for which the values in column 750 are to be displayed. So can the values in column 750 correspond to values in display sections 712, 714 or 730. Column 752 indicates action thresholds. The action thresholds in column 752 may be thresholds corresponding to the measured values in column 750. If the measured values in column 750 meet the corresponding action thresholds in column 752, then the control system 214 takes the action identified in column 754. In some cases, a measured value may meet a corresponding action threshold by meeting or exceeding the corresponding action threshold. For example, in one example, operator 260 may select a threshold to change the threshold by touching the threshold in column 752 . Once selected, the operator 260 can change the threshold. The thresholds in column 752 may be configured to perform the designated action when the measured value 750 exceeds, equals, or is below the threshold.

Likewise, the operator 260 can touch the action identifiers in column 754 to change the action to be taken. When a threshold is met, multiple actions can be taken. For example, at the bottom of column 754, a reduction in fan speed by 50 revolutions per minute (rpm) and a reduction in screen openings by 1 millimeter (mm) are identified as actions to be taken when the measured value in column 750 den Threshold in column 752 met.

The actions that can be specified in column 754 can be any of a variety of different types of actions. For example, the actions may include a hold action that, when performed, prevents the agricultural harvester 100 from continuing to harvest in an area. The actions may include a change speed action that, when performed, changes the ground speed of the agricultural harvesting machine 100 through the field or the speed of the cleaning fan 120 . The actions may include a setting change action to change a setting of an internal actuator or another WMA or set of WMAs, such as actuators changing sieve settings, chopper settings, concave gap, rotor speed. The actions may also include settings for implementing a change action that changes a setting of reel position or other WMAs. These are just examples and a variety of other actions are contemplated herein.

The indicator markers shown on the user interface display 720 can be controlled visually. Visually controlling the interface display 720 may be performed to capture the operator's 260 attention. For example, the indicator marks can be controlled to modify the intensity, color, or pattern with which the indicator marks are displayed. Additionally, the indicator markers can be controlled to blink. The changes described in the visual appearance of the indicator markings are provided by way of example. Consequently, other aspects of the visual appearance of the indicia may be altered. Thus, in various circumstances, the indicator markings may be modified in a desired manner, for example, to attract the operator's 260 attention.

Returning to the flow chart of 10 the description of the operation of the operator interface controller 231 continues. At block 760 , operator interface controller 231 recognizes an input setting of a flag and controls touch-sensitive user interface display 720 to display the flag on panel display portion 728 . The recognized input may be operator input, as indicated at 762 , or input from another controller, as indicated at 764 . At block 766 , operator interface controller 231 recognizes an in situ sensor input indicative of a measured feature of the field from one of in situ sensors 208 . At block 768, visual control signal generator 684 generates control signals to control user interface display 720 to display actuators, to modify user interface display 720, and to modify machine controls. For example, block 770 represents that one or more of the actuators for setting or modifying the values in columns 739, 746, and 748 may be displayed. Thus, the user can set flags and change the properties of those flags. For example, a user can change the core size and biomass level designators according to the flags. Block 772 represents that action thresholds in column 752 are displayed. Block 776 represents the actions in column 754 being displayed and block 778 represents the measured in situ data in column 750 being displayed. Block 780 indicates that a variety of other information and actuators may be displayed on user interface display 720 as well.

At block 782, operator input command processing system 654 captures and processes operator inputs corresponding to interactions with user interface display 720 performed by the operator 260 to be performed. When the user interface mechanism on which user interface display 720 is displayed is a touch-sensitive display screen, interaction inputs with the touch-sensitive display screen by operator 260 may be touch gestures 784 . In some cases, the operator interaction inputs may be inputs using a point and click device 786 or other operator interaction inputs 788 .

At block 790, operator interface controller 231 receives signals indicative of an alarm condition. For example, block 792 indicates that signals may be received from control input processing system 668 indicating sensed values in column 750 meet threshold conditions present in column 752 . As previously explained, the threshold conditions may include values that are below a threshold, at a threshold, or above a threshold. Block 794 shows that in response to receiving an alarm condition, the action signal generator 660 can alert the operator 260 using the visual control signal generator 684 to generate visual alarms, using the audio control signal generator 686 to generate audio alarms, using the haptic control signal generator 688 to generate haptic alerts, or by using any combination thereof. Likewise, as indicated by block 796, controller output generator 670 may generate outputs to other controllers in control system 214 so that those controllers perform the appropriate action identified in column 754. Block 798 indicates that operator interface controller 231 may recognize and process alarm conditions in other ways.

Block 900 shows that language processing system 662 can recognize and process input that calls into language processing system 658 . Block 902 shows that performing language processing may include using dialog management system 680 to perform a dialog with operator 260 . Block 904 shows that speech processing may include providing signals to control output generator 670 so that control operations are performed automatically based on the speech inputs.

Table 1 below shows an example dialog between operator interface control 231 and operator 260 . In the example shown in Table 1, the wake-up word is "Johnny".

Table 1

Operator: "Johnny, let me know the core size"

Operator interface control: "The current kernel size is large."

Table 2 shows an example where speech synthesis component 676 provides an output to audio control signal generator 686 to provide audible updates on an intermittent or periodic basis. The interval between updates may be time based, such as every five minutes, or coverage or distance based, such as every two hectares, or exception based, such as when a measured value is greater than a threshold.

Table2

Operator interface control: "In the last 10 minutes, the kernel size was 5% large, 80% medium, and 15% small."

Operator interface control: “The next 1 hectare predicted kernel size distribution is large, 10%; medium, 80%; and small, 10%.”

The example shown in Table 3 illustrates that some actuators or user input mechanisms on touch-sensitive display 720 may be supplemented with voice dialogue. The example in Table 3 illustrates that the action signal generator 660 can generate action signals to automatically mark a large kernel size location in the field to be harvested.

Table3

Human: "Johnny, mark a major kernel size spot."

Operator interface control: "Large kernel size digit marked."

The example presented in Table 4 illustrates that the action signal generator 660 may dialogue with the operator 260 to start and stop marking a large kernel size location.

Table 4

Human: "Johnny, start marking large core size spots."

Operator interface control: "Large kernel size location is highlighted."

Human: "Johnny, finish marking the large kernel size location."

Operator interface control: "Marking of a large core size location stopped."

The example shown in Table 5 illustrates that action signal generator 160 can generate signals to mark a small kernel size location in a manner different from those shown in Tables 3 and 4.

Table 5

Human: "Johnny, mark the next 30 meters as a small core size spot."

Operator Interface Control: "The next 30 meters are marked as a small core size spot."

Referring again to 10 Block 906 illustrates that operator interface controller 231 may otherwise detect and process conditions for outputting a message or other information. For example, the other controller interaction system 656 may recognize input from other controllers indicating that the operator 260 should be presented with alarms or output messages. Block 908 shows that the outputs can be audio messages. Block 910 shows that the outputs can be visual messages and block 912 shows that the outputs can be haptic messages. Until the operator interface controller 231 determines that the current harvesting operation is complete, as indicated by block 914, processing returns to block 698 where the geographical position of the harvester 100 is updated and processing continues as described above to the user interface display 720 To update.

Once the process is complete, any desired values that are displayed or have been displayed on the user interface display 720 can be saved. These values can also be used in machine learning to improve various sections of the predictive model generator 210, predictive map generator 212, control zone generator 213, control algorithms, or other elements. Block 916 indicates storage of the desired values. The values can be stored locally on the agricultural harvester 100 or the values can be stored at a remote server location or sent to another remote system.

Thus it can be seen that an information map is obtained from an agricultural harvester showing values at different geographic positions of a field to be harvested. An in situ sensor on the harvester detects a feature as the agricultural harvester moves through the field. A predictive map generator creates a predictive map that predicts control values for various positions based on the values in the information map and the feature sensed by the in situ sensor. A control system controls the controllable subsystem based on the values in the predictive map.

A control value is a value on which an action can be based. A control value, as described herein, may include any value (or characteristics indicated by or derived from the value) that can be used in controlling agricultural harvesting machine 100 . A control value can be any value that indicates an agricultural characteristic. A control value can be a predictive value, a measured value, or a recognized value. A control value may include any of the values provided by a card, such as any of the cards described herein, for example, a control value may be a value provided by an information card, a value provided by a preliminary information card, or a Value provided by a predictive map, such as a functional predictive map. A control value may also include any of the characteristics indicated by or derived from the values sensed by any of the sensors described herein. In other examples, a control value may be provided by an operator of the agricultural machine, such as a command input by an operator of the agricultural machine.

Processors and servers have been mentioned throughout this discussion. In one example, the processors and servers include computer processors with associated memory and timing circuitry, which are not shown separately. The processors and servers are functional parts of the systems or devices to which the processors and servers belong and through which they act four, and facilitate the functionality of the other components or elements in these systems.

A number of user interface displays were also discussed. The displays can take a number of different forms and can have a number of different user controlled operator interface mechanisms thereon. For example, the user-actuable operator interface mechanisms may include text boxes, check boxes, icons, links, drop-down menus, search boxes, and so on. The user actuatable operator interface mechanisms can also be actuated in a variety of ways. For example, the user actuable operator interface mechanisms can be operator interface mechanisms such as a point-and-click device, a trackball or mouse, hardware buttons, switches, a joystick or keyboard, thumb switches or thumbpads, etc., a virtual keyboard or other virtual Actuators are actuated. In addition, when the screen on which the user actuable operator interface mechanisms are displayed is a touch-sensitive screen, the user actuable operator interface mechanisms can be actuated with touch gestures. User actuatable operator interface mechanisms can also be actuated using voice commands with speech recognition functionality. Speech recognition can be implemented using a speech recognition device, such as a microphone, and software operable to recognize speech and execute commands based on the received speech.

A number of data stores were also discussed. It is pointed out that the data stores can each be divided into several data stores. In some examples, one or more of the data stores may be local to systems accessing the data stores, one or more of the data stores may be remote from a system using the data store, or one or more data stores may be local while others are remote. All of these configurations are contemplated by the present disclosure.

In addition, the figures show a series of blocks with functions associated with each block. It is noted that fewer blocks may be used to illustrate that functionality assigned to multiple different blocks is performed by fewer components. More blocks can also be used, illustrating that functionality can be distributed across multiple components. In different examples, some functionality may be added and some may be removed.

It should be noted that the discussion above has described a variety of different systems, components, logic and interactions. It is understood that any or all such systems, components, logic, and interactions are accomplished by hardware elements, such as processors, memory, or other processing components, including but not limited to artificial intelligence components, such as neural networks, some of which are described below can be implemented that perform the functions associated with these systems, components, logics or interactions. In addition, any or all of the systems, components, logic, and interactions may be implemented by software loaded into memory and then executed by a processor or server or other computing component, as described below. Any or all of the systems, components, logic, and interactions may also be implemented through various combinations of hardware, software, firmware, etc., some examples of which are described below. These are some examples of different structures that can be used to implement any or all of the systems, components, logic, and interactions described above. Other structures can also be used.

12 12 is a block diagram of agricultural harvester 600 shown in FIG 2 shown agricultural harvesting machine 100 may be similar. Agricultural harvester 600 communicates with elements in a remote server architecture 500. In some examples, remote server architecture 500 provides computing, software, data access, and storage services that do not require end-user knowledge of the physical location or configuration of the system that provides the Services. In various examples, remote servers may provide the services over a wide area network, such as the Internet, using appropriate protocols. For example, remote server applications can be deployed over a wide area network and accessed through a web browser or other computer component. Software or components included in 2 shown and related data may be stored on servers at a remote location. The computing resources in a remote server environment can be consolidated at a remote data center location or distributed to a variety of remote data centers. Remote server infrastructures can provide services across shared data centers, although the services appear to the user as a single point of entry. Thus, the components and functions described herein can be provided from a remote server at a remote site via a remote server architecture.

Alternatively, the components and functions may be provided by a server, or the components and functions may be installed directly or otherwise on end devices.

in the in 12 example shown are some elements those in 2 shown and these items are similarly numbered. 12 FIG. 12 specifically shows that the predictive model generator 210 or the predictive map generator 212 or both may be located at a server location 502 that is remote from the agricultural harvester 600. FIG. Therefore, the one in the example applies 12 shown agricultural harvester 600 via remote server location 502 to systems.

12 also illustrates another example of a remote server architecture. 12 shows that some elements of 2 may be located at a remote server location 502 while others may be located elsewhere. For example, data store 202 may be located at a separate site from site 502 and accessed through the remote server at site 502 . Regardless of where the elements are located, the elements can be accessed directly by the agricultural harvester 600 over a network, such as a wide area network or a local area network; the items may be hosted at a remote location by a service; or the items may be provided as a service or accessed via a connection service located at a remote location. In addition, data can be stored at any location and the stored data can be retrieved or forwarded to operators, users or systems. For example, physical carriers can be used instead of or in addition to electromagnetic radiation carriers. In some examples where network coverage is poor or non-existent, another machine, e.g. a tank truck or other mobile machine or vehicle, have an automatic, semi-automatic or manual information collection system. When the combine harvester apparatus 600 gets close to the machine containing the information gathering system, such as a tanker truck, prior to refueling, the information gathering system collects the information from the combine harvester 600 via any ad hoc wireless connection. The information collected may then be relayed to another network when the machine containing the received information reaches a location where wireless telecommunications service coverage or other wireless coverage is available. For example, a fuel truck may enter an area that has wireless communications coverage when it is traveling to a location to fuel other machines, or when it is at a main fuel depot. All of these architectures are considered herein. Furthermore, the information may be stored in the agricultural harvester 600 until the agricultural harvester 600 reaches an area of wireless communication coverage. The agricultural harvester 600 itself can send the information to another network.

It is also noted that the elements of 2 or portions thereof may be located on a variety of different devices. One or more of these devices may include an on-board computer, electronic control unit, display unit, server, desktop computer, laptop computer, tablet computer, or other mobile device, such as a palmtop computer, a mobile phone, a smartphone , a multimedia player, a personal digital assistant, etc.

In some examples, the remote server architecture 500 may include cyber security measures. Without limitation, these measures may include encryption of data on storage devices, encryption of data sent between network nodes, authentication of individuals or processes accessing data, and use of ledgers to record metadata, data, data transfers, data access and involve data transformations. In some examples, the ledgers may be distributed and immutable (eg, implemented as a blockchain).

13 1 is a simplified block diagram of an illustrative example of a portable or mobile computing device that may be used as user or customer handheld device 16 in which the present system (or portions thereof) may be employed. For example, a mobile device in the driver's ka may be employed within agricultural harvesting machine 100 to generate, process, or display the maps discussed above. the 14-15 are examples of portable or mobile devices.

13 shows a general block diagram of the components of a terminal 16, showing some of the 2 components shown can run, interact with them, or both. A communications link 13 is provided in device 16, enabling the portable device to communicate with other computing devices and, in some examples, providing a channel for automatically receiving information, such as by scanning. Examples of communication links 13 include allowing communication over one or more communication protocols, such as wireless services used to provide cellular access to a network, and protocols that provide local wireless connections to networks.

In other examples, applications may be received on a removable Secure Digital (SD) card connected to an interface 15 . Interface 15 and communications links 13 communicate with a processor 17 (which may also embody the processors or servers of the other FIGS.) via a bus 19 which is also connected to memory 21 and input/output (I/O) components 23 and the clock 25 and the locating system 27 is connected.

I/O components 23 are provided in one example to facilitate input and output operations. I/O components 23 for various examples of terminal device 16 may include input components such as buttons, tactile sensors, optical sensors, microphones, touch screens, proximity sensors, accelerometer sensors, orientation sensors, and output components such as a display, speaker, and/or printer port . Other I/O components 23 can also be used.

Clock 25 illustratively includes a real-time clock component that outputs a time and date. This may also provide timing functions for processor 17, illustratively.

The location system 27 illustratively includes a component that outputs a current geographic position of the device 16 . This may include, for example, a global positioning system (GPS) receiver, a LORAN system, a dead reckoning system, a cellular triangulation system, or other positioning system. For example, location system 27 may also include mapping or navigation software that generates desired maps, navigation routes, and other geographic features.

Memory 21 stores operating system 29, network settings 31, applications 33, application configuration settings 35, data storage 37, communication drivers 39, and communication configuration settings 41. Memory 21 may include all types of tangible volatile and non-volatile computer-readable storage devices. Memory 21 may also include computer storage media (see below). Memory 21 stores computer-readable instructions that, when executed by processor 17, cause the processor to perform computer-implemented steps or functions in accordance with the instructions. The processor 17 can be activated by other components to enhance their functionality as well.

14 FIG. 12 shows an example where the device 16 is a tablet computer 600. FIG. In 14 computer 601 with user interface screen 602 is shown. The screen 602 can be a touch screen or a pen-enabled interface that receives input from a pen or stylus. The tablet computer 600 can also use a virtual on-screen keyboard. Of course, the computer 601 can also be connected to a keyboard or other user input device via any suitable attachment mechanism, such as a wireless connection or a USB port. Computer 601 may illustratively also receive voice input.

15 is similar to 14 except that the device is a smartphone 71 . The smartphone 71 has a touch-sensitive display 73 that displays icons or graphics or other user input mechanisms 75 . The mechanisms 75 can be used by a user to run applications, make calls, perform data transfer operations, etc. In general, the smartphone 71 is built on top of a mobile operating system and offers more advanced computing power and connectivity than a feature phone.

It should be noted that other shapes of devices 16 are possible.

16 is an example of a computing environment in which elements of 2 used who can. With reference to 16 An exemplary system for implementing some embodiments includes a computing device in the form of a computer 810 programmed to operate as discussed above. The components of the computer 810 may include, but are not limited to, a processing unit 820 (which may include processors or servers from the preceding FIGURES), a system memory 830, and a system bus 821 that connects various system components including the system memory to the processing unit 820 couple. The system bus 821 can be any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, and a local bus with a variety of bus architectures. Memory and programs related to 2 may be described in appropriate parts of 16 be used.

Computer 810 typically includes multiple computer-readable media. Computer-readable media can be any available media that can be accessed by the computer 810 and includes both volatile and non-volatile media, removable and non-removable media. By way of example and not limitation, computer-readable media can include computer storage media and communications media. Computer storage media is distinct from and does not include a modulated data signal or carrier wave. Computer-readable media includes hardware storage media, having volatile and non-volatile, removable and non-removable media, implemented in any method or technology for storage of information, such as computer-readable instructions, data structures, program modules, or other data. Computational storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other storage technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cartridge, tape, disk storage or other magnetic storage device, or any other medium that can be used to store the desired information accessible via computer 810. Communication media may contain computer-readable instructions, data structures, program modules, or other data in a transport mechanism and includes any information delivery media. The term "modulated data signal" means a signal that has one or more of its characteristics adjusted or altered to encode information in the signal.

System memory 830 includes computer storage media in the form of volatile and/or non-volatile memory, or both, such as read-only memory (ROM) 831 and random access memory (RAM) 832. A basic input/output system 833 (BIOS) that contains the basic programs that help To transfer information between elements within the computer 810, such as at startup, is typically stored in ROM 831. RAM 832 typically contains data or program modules, or both, that are immediately accessible to processing unit 820 and/or are currently operating. Illustrated by way of example and not limitation 16 the operating system 834, the application programs 835, further program modules 836 and program data 837.

Computer 810 may also include other removable/non-removable volatile/non-volatile computer storage media. For example shows 16 a hard disk drive 841 that reads from or writes to non-removable, non-volatile magnetic media, an optical disk drive 855, and a non-volatile optical disk 856. Hard disk drive 841 is typically connected to system bus 821 via a non-removable storage interface, such as interface 840, and optical disk drive 855 is typically connected to system bus 821 via a removable storage interface, such as interface 850.

Alternatively or additionally, the functionality described herein may be performed at least in part by one or more hardware logic components. Illustrative types of hardware logic components that may be used include, for example, field-programmable gate arrays (FPGAs), application-specific integrated circuits (e.g., ASICs), off-the-shelf application-specific products (e.g., ASSPs), system -on-a-chip systems (SOCs), "Complex Programmable Logic Devices" (CPLDs), etc.

The drives and associated computer storage media discussed above and in 16 11-11 provide storage for computer 810 of computer-readable instructions, data structures, program modules, and other data. In 16 For example, the hard disk drive 841 is shown as storing operating system 844, application programs 845, other program modules 846 and program data 847. It should be noted that these components may be either the same as or different from the operating system 834, application programs 835, other program modules 836, and program data 837.

A user may enter commands and information into the computer 810 through input devices such as a keyboard 862, a microphone 863, and a pointing device 861 such as a mouse, trackball, or touchpad. Other input devices (not shown) may include a joystick, gamepad, satellite dish, scanner, or the like. These and other input devices are often connected to the processing unit 820 via a user input interface 860, which is coupled to the system bus, but may also be connected via other interface and bus structures. A visual display 891 or other type of display device is also connected to the system bus 821 via an interface such as a video interface 890 . In addition to the monitor, computers may also include other peripheral output devices, such as speakers 897 and printer 896, which may be connected via an output peripheral interface 895.

Computer 810 operates in a network environment via logical connections (such as CAN, LAN, or WAN) to one or more remote computers, such as remote computer 880.

When used in a LAN network environment, the computer 810 is connected to the LAN 871 via a network interface or adapter 870 . When used in a WAN network environment, the computer 810 typically includes a modem 872 or other means for establishing communication over the WAN 873, such as the Internet. In a networked environment, program modules may be stored on an external storage device. 16 illustrates, for example, that remote application programs 885 can reside on remote computer 880 .

It should also be noted that the various examples described here can be combined in various ways. That is, portions of one or more examples may be combined with portions of one or more other examples. All of these are considered here.

• ein Erntegutverarbeitungssystem;
• ein Kommunikationssystem, das eine Informationskarte empfängt, die Werte eines ersten landwirtschaftlichen Merkmals beinhaltet, die verschiedenen geografischen Positionen in einem Feld entsprechen;
• einen geografischen Positionssensor, der eine geografische Position der landwirtschaftlichen Arbeitsmaschine erfasst;
• einen In-situ-Sensor, der einen Wert eines zweiten landwirtschaftlichen Merkmals erkennt, das ein Merkmal eines geernteten Materials angibt, das der geografischen Position entspricht;
• einen prädiktiven Kartengenerator, der eine funktionelle prädiktive landwirtschaftliche Karte des Feldes erzeugt, die prädiktive Werte des zweiten landwirtschaftlichen Merkmals auf Grundlage der Werte des ersten landwirtschaftlichen Merkmals in der Informationskarte auf die verschiedenen geografischen Positionen in dem Feld abbildet; und
• ein Steuersystem, das das Erntegutverarbeitungssystem auf Grundlage der prädiktiven Werte des zweiten landwirtschaftlichen Merkmals an den verschiedenen Positionen in dem Feld und auf Grundlage der erkannten geografischen Position steuert.

Example 1 is an agricultural working machine comprising:

• a crop processing system;
• a communication system that receives an information map that includes values of a first agricultural characteristic that correspond to different geographic locations in a field;
• a geographic position sensor that detects a geographic position of the agricultural working machine;
• an in situ sensor that detects a value of a second agricultural trait indicative of a trait of a harvested material that corresponds to the geographic location;
• a predictive map generator that generates a functional predictive agricultural map of the field that maps predictive values of the second agricultural feature based on the values of the first agricultural feature in the information map to the different geographic locations in the field; and
• a control system that controls the crop processing system based on the predicted values of the second agricultural characteristic at the different positions in the field and based on the recognized geographic position.

• einen prädiktiven Modellgenerator, der ein prädiktives landwirtschaftliches Modell erzeugt, das eine Beziehung zwischen dem ersten landwirtschaftlichen Merkmal und dem zweiten landwirtschaftlichen Merkmal auf Grundlage eines Werts des ersten landwirtschaftlichen Merkmals in der Informationskarte an der geografischen Position und eines Werts des zweiten landwirtschaftlichen Merkmals modelliert, das durch den In-situ-Sensor an der geografischen Position erfasst wird, wobei der prädiktive Kartengenerator die funktionelle prädiktive landwirtschaftliche Karte des Feldes erzeugt, die prädiktive Werte des zweiten landwirtschaftlichen Merkmals auf Grundlage der Werte des ersten landwirtschaftlichen Merkmals in der Informationskarte und auf Grundlage des prädiktiven landwirtschaftlichen Modells auf die verschiedenen geografischen Positionen in dem Feld abbildet.

Example 2 is the agricultural work machine of any or all of the preceding examples and further comprising:

• a predictive model generator that generates a predictive agricultural model that models a relationship between the first agricultural feature and the second agricultural feature based on a value of the first agricultural feature in the information map at the geographic location and a value of the second agricultural feature that is determined by detecting the in situ sensor at the geographic position, wherein the predictive map generator generates the functional predictive agricultural map of the field that includes predictive values of the second agricultural feature based on the values of the first agricultural feature in the information map and based on the predictive agricultural model to the different geographic locations in the field.

• einen Kerngrößensensor, der ein Kerngrößenmerkmal von Kernen in der landwirtschaftlichen Arbeitsmaschine erfasst.

Example 3 is the agricultural work machine of any or all of the preceding examples, wherein the harvested material includes cores, and wherein the in situ sensor includes:

• a kernel size sensor that detects a kernel size characteristic of kernels in the agricultural work machine.

• einen EHP-Merkmalsensor, der ein EHP-Merkmal erfasst, das ein Merkmal von einem oder mehreren von Ähren, Köpfen und Hülsen in der landwirtschaftlichen Arbeitsmaschine anzeigt.

Example 4 is the agricultural work machine of any or all of the preceding examples, wherein the harvested material is at least includes one of ears, heads and husks (EHP) and wherein the in situ sensor includes:

• an EHP trait sensor that senses an EHP trait indicative of a trait of one or more of ears, heads, and pods in the agricultural work machine.

• einen Kerngrößenkartengenerator, der als funktionelle prädiktive landwirtschaftliche Karte eine prädiktive Kerngrößenkarte erzeugt, die als prädiktive Werte des zweiten landwirtschaftlichen Merkmals prädiktive Werte der Größe der Kerne auf Grundlage der Werte des ersten landwirtschaftlichen Merkmals in der Informationskarte auf die verschiedenen geografischen Positionen in dem Feld abbildet.

Example 5 is the agricultural work machine of any or all of the preceding examples, wherein the harvested material includes cores, and wherein the predictive map generator includes:

• a kernel size map generator which, as a functional predictive agricultural map, generates a predictive kernel size map which, as predictive values of the second agricultural characteristic, maps predictive values of the size of the kernels based on the values of the first agricultural characteristic in the information map to the different geographic positions in the field.

• einen EHP-Merkmalskartengenerator, der als die funktionelle prädiktive landwirtschaftliche Karte eine prädiktive EHP-Karte erzeugt, die als prädiktive Werte des zweiten landwirtschaftlichen Merkmals prädiktive Werte von einem oder mehreren EHP-Merkmalen auf Grundlage der Werte des ersten landwirtschaftlichen Merkmals in der Informationskarte auf die verschiedenen geografischen Positionen in dem Feld abbildet.

Example 6 is the agricultural work machine of any or all of the preceding examples, wherein the harvested material includes at least one of ears, heads, and husks (EHP), and wherein the predictive map generator includes:

• an EHP trait map generator which, as the functional predictive agricultural map, generates a predictive EHP map which as predictive values of the second agricultural trait predictive values of one or more EHP traits based on the values of the first agricultural trait in the information map to the different geographical positions in the field.

• einen Kombinationskartengenerator, der als die funktionelle prädiktive Karte eine prädiktive Kombinationskarte erzeugt, die die prädiktiven Werte des zweiten landwirtschaftlichen Merkmals auf Grundlage des prädiktiven landwirtschaftlichen Modells und auf Grundlage der Werte des ersten landwirtschaftlichen Merkmals in der ersten Informationskarte auf die verschiedenen geografischen Positionen in dem Feld abbildet.

Example 7 is the agricultural work machine of any or all of the preceding examples, wherein the communication system receives as an information card a first information card containing the values of the first agricultural characteristic and a second information card containing the values of a third agricultural characteristic, the various positions in the field correspond,
wherein the predictive model generator generates the predictive agricultural model to establish a relationship between the second agricultural attribute and a combination of the first and third agricultural attributes based on the value of the second agricultural attribute detected by the in situ sensor at the geographic location , and to model based on the value of the first agricultural feature in the first information map at the geographic location and the value of the third agricultural feature in the second information map at the geographic location, and
wherein the predictive map generator comprises:

• a combination map generator that generates, as the functional predictive map, a combination predictive map that maps the predicted values of the second agricultural feature based on the predictive agricultural model and based on the values of the first agricultural feature in the first information map to the different geographical positions in the field .

Example 8 is the agricultural working machine of any or all of the preceding examples, the communication system receiving as the advance information map a seed genotype map that includes a seed genotype as the first agricultural characteristic, the predictive model generator generating the predictive agricultural model to establish a relationship between the seed genotype and the second agricultural trait to model.

Example 9 is the agricultural working machine of one or all of the preceding examples, the communication system receiving a vegetative index card as an information map, which includes a vegetative index trait as the first agricultural trait, the predictive model generator generating the predictive agricultural model to establish a relationship between the vegetative index trait and to model the second agricultural characteristic.

Example 10 is the agricultural work machine of any or all of the preceding examples, wherein the communication system receives as the information map a yield map that includes a predictive yield trait as the first agricultural trait, the predictive model generator generating the predictive agricultural model to establish a relationship between the predictive yield trait and to model the second agricultural characteristic.

Example 11 is the agricultural work machine of any or all of the preceding examples, the communication system receiving as the information map a biomass map that includes a biomass feature as the first agricultural feature, the predictive model generator generating the predictive agricultural model to establish a relationship between the biomass feature and the second to model agricultural traits.

• Empfangen einer Informationskarte in der landwirtschaftlichen Arbeitsmaschine, die Werte eines ersten landwirtschaftlichen Merkmals angibt, die verschiedenen geografischen Positionen in einem Feld entsprechen;
• Erkennen einer geografischen Position der landwirtschaftlichen Arbeitsmaschine;
• Erkennen eines zweiten landwirtschaftlichen Merkmals mit einem In-situ-Sensor, das ein Merkmal eines Ernteguts angibt, das der geografischen Position entspricht;
• Steuern eines prädiktiven Kartengenerators, um eine funktionelle prädiktive landwirtschaftliche Karte des Feldes zu erzeugen, die prädiktive Werte des zweiten landwirtschaftlichen Merkmals auf Grundlage der Werte des ersten landwirtschaftlichen Merkmals auf der Informationskarte abbildet; und
• Steuern des Erntegutverarbeitungssystems auf Grundlage der prädiktiven Werte des zweiten landwirtschaftlichen Merkmals an den verschiedenen Positionen in dem Feld und auf Grundlage der erkannten geografischen Position.

Example 12 is a computer-implemented method for controlling an agricultural work machine that includes a crop processing system, comprising:

• receiving, at the agricultural work machine, an information map indicating values of a first agricultural characteristic corresponding to different geographic locations in a field;
• detecting a geographic location of the agricultural work machine;
• detecting, with an in situ sensor, a second agricultural characteristic indicative of a characteristic of a crop corresponding to the geographic location;
• controlling a predictive map generator to generate a functional predictive agricultural map of the field mapping predictive values of the second agricultural feature based on the values of the first agricultural feature on the information map; and
• Controlling the crop processing system based on the predicted values of the second agricultural characteristic at the different locations in the field and based on the identified geographic location.

• Erzeugen eines prädiktiven landwirtschaftlichen Modells, das eine Beziehung zwischen dem ersten landwirtschaftlichen Merkmal und dem zweiten landwirtschaftlichen Merkmal auf Grundlage eines Werts des ersten landwirtschaftlichen Merkmals in der Informationskarte an der geografischen Position und eines Werts des zweiten landwirtschaftlichen Merkmals modelliert, der durch den In-situ-Sensor an der geografischen Position erfasst wird, wobei das Steuern des prädiktiven Kartengenerators die funktionelle prädiktive landwirtschaftliche Karte des Feldes erzeugt, die prädiktive Werte des zweiten landwirtschaftlichen Merkmals auf Grundlage der Werte des ersten landwirtschaftlichen Merkmals in der Informationskarte und auf Grundlage des prädiktiven landwirtschaftlichen Modells auf die verschiedenen geografischen Positionen in dem Feld abbildet.

Example 13 is the computer-implemented method of any or all of the preceding examples, and further includes:

• generating a predictive agricultural model that models a relationship between the first agricultural feature and the second agricultural feature based on a value of the first agricultural feature in the information map at the geographic location and a value of the second agricultural feature obtained by the in situ Sensor is detected at the geographic position, wherein controlling the predictive map generator produces the functional predictive agricultural map of the field, the predictive values of the second agricultural feature based on the values of the first agricultural feature in the information map and based on the predictive agricultural model on the different geographical positions in the field.

• Erkennen eines Korngrößenmerkmals von Kernen in der landwirtschaftlichen Arbeitsmaschine.

Example 14 is the computer-implemented method of any or all of the preceding examples, wherein the harvested material includes kernels, and wherein recognizing a second agricultural trait comprises:

• Recognizing a grain size feature of kernels in the agricultural working machine.

• Erkennen eines EHP-Merkmals, das ein Merkmal eines oder mehrerer von Ähren, Köpfen und Hülsen in der landwirtschaftlichen Arbeitsmaschine anzeigt.

Example 15 is the computer-implemented method of any or all of the preceding examples, wherein the harvested material comprises at least one of ears, heads, and husks (EHP), and wherein detecting the second agricultural trait comprises:

• detecting an EHP trait indicative of a characteristic of one or more ears, heads, and pods in the agricultural work machine.

• Steuern eines Kerngrößenkartengenerators, der als funktionelle prädiktive landwirtschaftliche Karte eine prädiktive Kerngrößenkarte erzeugt, die als prädiktive Werte des zweiten landwirtschaftlichen Merkmals prädiktive Werte der Kerngröße auf Grundlage der Werte des ersten landwirtschaftlichen Merkmals in der Informationskarte auf die verschiedenen geografischen Positionen in dem Feld abbildet.

Example 16 is the computer-implemented method of any or all of the preceding examples, wherein the harvested material includes cores, and wherein controlling the predictive map generator includes:

• Controlling a core size map generator which, as a functional predictive agricultural map, generates a predictive core size map which, as predictive values of the second agricultural feature, maps predictive values of the core size based on the values of the first agricultural feature in the information map to the different geographic positions in the field.

• Steuern eines EHP-Merkmalkartengenerators, der als die funktionelle prädiktive landwirtschaftliche Karte eine prädiktive EHP-Karte erzeugt, die als prädiktive Werte des zweiten landwirtschaftlichen Merkmals prädiktive Werte auf Grundlage der Werte des ersten landwirtschaftlichen Merkmals in der Vorabinformationskarte und auf Grundlage des prädiktiven Modells auf die verschiedenen geografischen Positionen in dem Feld abbildet.

Example 17 is the computer-implemented method of any or all of the preceding examples, wherein the harvested material includes at least one of ears, heads, and husks (EHP), and wherein controlling the predictive map generator includes:

• Controlling an EHP feature map generator which, as the functional predictive agricultural map, generates a predictive EHP map which, as predictive values of the second agricultural feature, predictive values based on the values of the first agricultural feature in the prior information map and on the basis of the predictive model on the various geographical positions in the field.

• Steuern des prädiktiven Kartengenerators, um als die funktionelle prädiktive Karte eine prädiktive Kombinationskarte zu erzeugen, die die prädiktiven Werte des zweiten landwirtschaftlichen Merkmals auf Grundlage des prädiktiven landwirtschaftlichen Modells und auf Grundlage der Werte des ersten landwirtschaftlichen Merkmals in der ersten Informationskarte und auf Grundlage der Werte des dritten landwirtschaftlichen Merkmals in der zweiten Informationskarte auf die verschiedenen geografischen Positionen in dem Feld abbildet.

Example 18 is the computer-implemented method of any or all of the preceding examples, wherein receiving the information card comprises receiving a first information card as an information card that includes values of the first agricultural feature and a second information card that includes values of a third agricultural feature that is different corresponds to positions in the field, and wherein generating the predictive agricultural model comprises generating the predictive agricultural model to establish a relationship between the second agricultural feature and a combination of the first and third agricultural features based on the value of the second agricultural feature detected by the in situ sensor at the geographic location and based on the value of the first agricultural feature in the first modeling the information map at the geographic location and the value of the third agricultural feature in the second information map at the geographic location, and wherein controlling the predictive map generator comprises:

• Controlling the predictive map generator to generate, as the functional predictive map, a combination predictive map that includes the predictive values of the second agricultural feature based on the predictive agricultural model and based on the values of the first agricultural feature in the first information map and based on the values of the third agricultural feature in the second information map to the different geographical positions in the field.

Example 19 is the computer-implemented method of any or all of the preceding examples, wherein the communication system receives as an information map one or more of a seed genotype map that includes a seed genotype as a first agricultural trait; a vegetative index map including as a first agricultural trait a vegetative index trait; a yield map including a predictive yield trait as a first agricultural trait; a biomass map that contains a biomass characteristic as the first agricultural characteristic.

• ein Kommunikationssystem, das eine Informationskarte empfängt, die Werte eines ersten landwirtschaftlichen Merkmals beinhaltet, die verschiedenen geografischen Positionen in einem Feld entsprechen;
• einen geografischen Positionssensor, der eine geografische Position der landwirtschaftlichen Arbeitsmaschine erfasst;
• einen In-situ-Sensor, der einen Wert eines zweiten landwirtschaftlichen Merkmals erkennt, das der geografischen Position entspricht, wobei das zweite landwirtschaftliche Merkmal ein oder mehrere einer Kerngröße von Kernen in der landwirtschaftlichen Arbeitsmaschine, ein Merkmal von Ähren in der landwirtschaftlichen Arbeitsmaschine, ein Merkmal von Köpfen in der landwirtschaftlichen Arbeitsmaschine und ein Merkmal von Hülsen in der landwirtschaftlichen Arbeitsmaschine angibt;
• einen prädiktiven Kartengenerator, der eine funktionelle prädiktive landwirtschaftliche Karte des Feldes erzeugt, die prädiktive Werte des zweiten landwirtschaftlichen Merkmals auf Grundlage der Werte des ersten landwirtschaftlichen Merkmals in der Informationskarte auf die verschiedenen geografischen Positionen in dem Feld abbildet; und
• ein Steuersystem, das Steuersignale erzeugt, um ein steuerbares Teilsystem auf der landwirtschaftlichen Arbeitsmaschine auf Grundlage der funktionellen prädiktiven landwirtschaftlichen Karte zu steuern.

Example 20 is an agricultural work machine comprising:

• a communication system that receives an information map that includes values of a first agricultural characteristic that correspond to different geographic locations in a field;
• a geographic position sensor that detects a geographic position of the agricultural working machine;
• an in situ sensor that detects a value of a second agricultural trait corresponding to the geographic location, the second agricultural trait being one or more of a kernel size of kernels in the agricultural work machine, a trait of ears of corn in the agricultural work machine, a trait of heads in the agricultural working machine and a feature of sleeves in the agricultural working machine;
• a predictive map generator that generates a functional predictive agricultural map of the field that maps predictive values of the second agricultural feature based on the values of the first agricultural feature in the information map to the different geographic locations in the field; and
• a control system that generates control signals to control a controllable subsystem on the agricultural work machine based on the functional predictive agricultural map.

Although the subject matter has been described in language specific to structural features and/or methodical acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above are described. Rather, the features and acts described above are disclosed as example forms of the claims.

Claims (15)

Agricultural working machine (100), comprising: a crop processing system; a communication system (206) receiving an information map (258) including values of a first agricultural characteristic corresponding to different geographic locations in a field; a geographic position sensor (204) that detects a geographic position of the agricultural work machine (100); an in situ sensor (208) that detects a value of a second agricultural characteristic indicative of a characteristic of a harvested material that corresponds to the geographic location; a predictive map generator (212) that generates a functional predictive agricultural map of the field that maps predictive values of the second agricultural feature based on the values of the first agricultural feature in the information map (258) to the different geographic locations in the field; and a control system (214) that controls the crop processing system based on the predicted values of the second agricultural characteristic at the different locations in the field and based on the identified geographic location. Agricultural working machine after claim 1 and further comprising: a predictive model generator that generates a predictive agricultural model that generates a relationship between the first agricultural feature and the second agricultural feature based on a value of the first agricultural feature in the information map at the geographic location and a value of the second agricultural feature modeled, which is detected by the in situ sensor at the geographic position, wherein the predictive map generator generates the functional predictive agricultural map of the field, the predictive values of the second agricultural feature based on the values of the first agricultural feature in the information map and on based on the predictive agricultural model to the different geographic locations in the field. Agricultural working machine after claim 1 wherein the harvested material includes kernels, the in situ sensor comprising: a kernel size sensor that senses a kernel size characteristic of kernels in the agricultural work machine. Agricultural working machine after claim 1 wherein the harvested material includes at least one of ears, heads and husks (EHP) and wherein the in situ sensor comprises: an EHP trait sensor that detects an EHP trait that is a trait of one or more of ears, Indicates heads and sleeves in the agricultural working machine. Agricultural working machine after claim 2 , wherein the harvested material includes kernels, and wherein the predictive map generator comprises: a kernel size map generator which, as a functional predictive agricultural map, generates a predictive kernel size map having as predictive values of the second agricultural characteristic predictive values of the size of the kernels based on the values of the first agricultural Feature in the information map is mapped to the different geographic locations in the field. Agricultural working machine after claim 2 wherein the harvested material includes at least one of ears, heads and husks (EHP) and wherein the predictive map generator comprises: an EHP trait map generator generating as the functional predictive agricultural map an EHP predictive map used as predictive values of the second agricultural characteristic maps predictive values of one or more EHP characteristics based on the values of the first agricultural characteristic in the information map to the different geographical positions in the field. Agricultural working machine after claim 2 , wherein the communication system receives as an information map a first information map containing the values of the first agricultural characteristic and a second information map containing values of a third agricultural characteristic corresponding to different positions in the field, the predictive model generator generating the predictive agricultural model to determine a relationship between the second agricultural characteristic and a combination of the first and third agricultural characteristics based on the value of the second agricultural characteristic detected by the in situ sensor at the geographic location and based on the value of the first agricultural characteristic feature in the first information map at the geographic location and the value of the third agricultural feature in the second information map at the geographic location, and wherein the predictive map generator follows which comprises: a combination map generator that generates, as the functional predictive map, a predictive combination map that includes the predictive values of the second agricultural feature based on the predictive agricultural model and based on the values of the first agricultural feature in the first information map and on the different geographic locations depicted in the field. Agricultural working machine after claim 2 wherein the communication system receives as the preliminary information map a seed genotype map including a seed genotype as a first agricultural trait, wherein the predictive model generator generates the predictive agricultural model to model a relationship between the seed genotype and the second agricultural trait. Agricultural working machine after claim 2 , wherein the communication system receives as an information map a vegetative index map that includes a vegetative index trait as the first agricultural trait, wherein the predictive model generator generates the predictive agricultural model to establish a relationship between the vegetative index trait and the second agricultural trait to model. Agricultural working machine after claim 2 , wherein the communication system receives as an information map a yield map that includes a predictive yield trait as the first agricultural trait, wherein the predictive model generator generates the predictive agricultural model to model a relationship between the predictive yield trait and the second agricultural trait. Agricultural working machine after claim 2 , wherein the communication system receives as the information map a biomass map including a biomass trait as the first agricultural trait, wherein the predictive model generator generates the predictive agricultural model to model a relationship between the biomass trait and the second agricultural trait. A computer-implemented method for controlling an agricultural work machine (100) that includes a crop processing system, comprising: receiving an information map (258) at the agricultural work machine (100) indicating values of a first agricultural characteristic corresponding to various geographic locations in a field; detecting a geographic position of the agricultural work machine (100); detecting a second agricultural characteristic with an in situ sensor (208) indicative of a characteristic of harvested material corresponding to the geographic location; controlling a predictive map generator (212) to produce a functional predictive agricultural map of the field mapping predicted values of the second agricultural feature based on the values of the first agricultural feature in the information map (258) to the various locations in the field; and Controlling the crop processing system based on the predicted values of the second agricultural characteristic at the different locations in the field and based on the identified geographic location. Computer-implemented method claim 12 , further comprising: generating a predictive agricultural model that models a relationship between the first agricultural feature and the second agricultural feature based on a value of the first agricultural feature in the information map at the geographic location and a value of the second agricultural feature determined by the In situ sensor is detected at the geographic position, wherein controlling the predictive map generator produces the functional predictive agricultural map of the field, the predictive values of the second agricultural feature based on the values of the first agricultural feature in the information map and based on the predictive agricultural model to the different geographic locations in the field. Computer-implemented method claim 12 wherein the harvested material includes kernels and wherein detecting a second agricultural characteristic comprises: detecting a kernel size characteristic of kernels in the agricultural work machine. Agricultural working machine (100), comprising: a communication system (306) receiving an information map (258) including values of a first agricultural characteristic corresponding to different geographic locations in a field; a geographic position sensor (204) that detects a geographic position of the agricultural work machine; an in situ sensor (208) detecting a value of a second agricultural characteristic corresponding to the geographic location, the second agricultural characteristic being one or more of a kernel size of kernels in the agricultural work machine, a characteristic of ears of corn in the agricultural work machine work machine (100), an attribute of heads in the agricultural work machine (100) and an attribute of sleeves in the agricultural work machine (100); a predictive map generator (212) that generates a functional predictive agricultural map of the field that maps predictive values of the second agricultural feature based on the values of the first agricultural feature in the information map (258) to the different geographic locations in the field; and a control system (214) that generates control signals to control a controllable subsystem (216) in the agricultural work machine (100) based on the functional predictive agricultural map.

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